# MyCustomAI - Full LLM Context
Site: https://www.mycustomai.io
Generated: 2026-06-12T14:10:06.598Z
## Services
### AI Feasibility & ROI
URL: https://www.mycustomai.io/services/ai-feasibility
Summary: A 6-8 week portfolio-level engagement that scores your AI use cases by value, feasibility, and risk — with a board-ready ROI case and buy/build/wait recommendation.
## When this is the right engagement
You'd take a Feasibility & ROI engagement instead of jumping straight to an AI Pilot when:
- **You're weighing multiple use cases** and need to know which to fund first — and what each will return.
- **Your board or finance committee is asking for an ROI case** before AI budget is approved.
- **You need a unified compliance and security framework** before any system goes live.
- **You want an objective vendor shortlist** that respects your regulatory and architectural constraints.
If you already know the use case and want to validate it with working code, skip this and start with an [AI Pilot](/services/ai-pilot).
## How it works
For organizations with a focused scope (fewer business units, or a pre-identified shortlist of use cases), we can compress weeks 1-4 and deliver in 6 weeks. Multi-business-unit organizations sometimes extend to 10-12 weeks.
## What you walk away with
A practical, defensible package your leadership can act on:
- A scored portfolio of use cases ranked by value, feasibility, risk, and time-to-impact
- An ROI model and payback estimate for each prioritized use case
- A compliance and architecture framework that addresses HIPAA, SOC 2, GDPR, FERPA, FINRA, or PCI as applicable
- Buy/build/wait recommendations with rationale and resourcing estimates
- A board-ready summary plus per-use-case 1-pagers your teams can use to build the internal business case
## Why customers pick us for this
The same engineers who model the ROI also write the code if you proceed. There's no advisory-to-delivery handoff and no incentive to over-promise — our deliverables regularly include "buy" and "wait" recommendations where we don't get a follow-on build. The ROI case you walk away with is pricing-aware, dependency-aware, and architecture-aware from day one — because it's built by the people who would do the build.
### How we build the ROI estimates
Our ROI models are bottoms-up: we estimate cost from actual infrastructure pricing, labor from our own build benchmarks, and value from your operational data (processing volumes, cycle times, error rates). Each estimate carries an explicit confidence band — high-confidence where we have comparable build history, lower where assumptions dominate. We don't publish a single magic number; we give you a range and tell you which inputs drive the spread. When a subsequent Pilot or Build validates or invalidates the forecast, we feed that back into the model — so clients who continue with us get progressively tighter estimates.
Feasibility & ROI is a fixed-fee engagement scoped to your organization. We share an indicative price band on the first discovery call so you can qualify budget internally before committing to a second conversation.
### AI Pilot
URL: https://www.mycustomai.io/services/ai-pilot
Summary: A hands-on feasibility study with working code on a sample of your real data, in 4-6 weeks. Captures both 'feasibility' and 'pilot' / 'PoC' searches.
## What an AI Pilot actually is
Most "feasibility studies" in AI are slide decks: a competitive landscape, a vendor comparison, and a hand-wavy "yes, this should work." Ours are different.
In 4-6 weeks, we deliver:
- **Working code** running on a sample of your actual data — not a synthetic dataset, not a public benchmark.
- **A quantitative evaluation** against a success metric we agreed to in week 1.
- **An architecture sketch** that shows how the prototype becomes production, including the deployment pattern and compliance posture.
- **A go/no-go recommendation** with a real estimate for the full Build.
If "AI Pilot" doesn't translate at your organization, it's exactly the same thing as what your procurement team will call a **feasibility study** or a **proof of concept**. We use both terms interchangeably with customers; the deliverable is the same.
## When this is the right engagement
- You have a specific use case in mind and need to validate that AI can actually solve it on *your* data.
- Your team has tried prompts in ChatGPT or Claude, and you want to know whether a real production system is feasible — and what it would cost.
- A vendor has pitched you, and you want a neutral, hands-on validation before committing.
- You need to convince a skeptic (a CFO, a CISO, a board member) with evidence, not narrative.
## How it works
## What we deliver
You walk away with:
1. **The prototype itself** — code, configuration, evaluation harness — handed to your team.
2. **An evaluation report** showing how the prototype performed on real data against the metric we agreed to.
3. **An architecture document** showing how this becomes a production system inside your security boundary.
4. **A full-build estimate** with timeline, scope, and pricing options.
5. **A documented compliance posture** matched to your regulatory footprint.
## Why we believe in hands-on feasibility
A slide deck can prove that something is *theoretically* possible. Only working code can prove it's possible *on your data, with your latency budget, at your accuracy bar*. Most failed AI projects we've inherited from other vendors fail because that step was skipped.
The original feasibility study said it would work. They never actually ran it on our documents.
## How this differs from AI Feasibility & ROI
An [AI Feasibility & ROI](/services/ai-feasibility) engagement is portfolio-level: it scores *many* use cases and produces an ROI case before any code is written. An AI Pilot is use-case-level: it takes *one* use case and proves (or disproves) it with working code on your data. If you're still deciding *which* problem to solve first, start with Feasibility & ROI. If you already know the problem, start here.
## What happens next
About 80% of AI Pilots graduate into a full [Custom AI Build](/services/custom-ai-build) plus [Private AI Deployment](/services/private-ai-deployment). The remaining 20% end with a "no-go" recommendation — and that's the value: catching the dead-end at week 6 instead of month 6.
### Custom AI Build
URL: https://www.mycustomai.io/services/custom-ai-build
Summary: Model selection, fine-tuning, RAG, agents, and end-to-end integration. The full-build engagement most customers move into after an AI Pilot.
## What "Custom AI Build" actually covers
Most production AI systems are not just a model. They're a stack:
We build all of it. The end state is a production system, not a demo.
## When this is the right engagement
- You've completed an [AI Pilot](/services/ai-pilot) (with us, or with another vendor) and want to ship the validated use case to production.
- You have a real production target with users who will rely on the system.
- You need integration with existing systems (EHR, DMS, SIEM, CRM, e-commerce platform, etc.) that goes beyond a AI Agent wrapper.
If you're still validating feasibility, start with a Pilot. If the system is already in production but needs ongoing improvement, look at [Managed AI](/services/managed-ai).
## How it works
A typical 4-month Build:
- **Month 1: Foundations.** Model selection, retrieval architecture, evaluation harness, success-criteria contract.
- **Month 2-3: Build.** Iteration cycles against the evaluation harness. Integration with your systems. Internal alpha.
- **Month 3-4: Hardening.** Performance, cost, security review, guardrails, observability. User-facing alpha or beta.
- **Handoff.** Documentation, evaluation harness, and runbook handed to your engineering team.
Most Builds are paired with [Private AI Deployment](/services/private-ai-deployment) — the deployment shape is part of the architecture from day one.
## How we work with your team
We're not a black-box vendor. We work alongside your engineers — pairing, code review, shared repo, regular demos. The goal is that your team can extend, evaluate, and operate the system on day one of production. That's also the foundation that [Training & Enablement](/services/training-and-enablement) and [Managed AI](/services/managed-ai) are built on.
Unless there's a strong business reason otherwise, we build on open-weight models (Llama, Mistral, Qwen, and similar). Open weights mean you can fine-tune privately, deploy in any region, and avoid surprise API price changes from a third-party model provider.
### Managed AI & MLOps
URL: https://www.mycustomai.io/services/managed-ai
Summary: Production monitoring, drift detection, retraining, compliance reporting, and incident response for AI systems running inside your security boundary.
## What "Managed AI" actually does
Production AI is not "fire and forget." Models drift. Data drifts. Compliance frameworks change. New, more capable models become available. Adversarial behavior emerges.
Managed AI covers the operational side of running production AI:
## When this is the right engagement
- You have AI in production (built by us or by your own team) and need ongoing operational support.
- You need a defensible audit story for SOC 2, HIPAA, or sector-specific regulators.
- Your team owns the system but doesn't have AI-specialist oncall coverage.
- You want to keep the system on the frontier — quarterly model refreshes, capability upgrades, evals against the latest benchmarks.
## Tiers
We offer three engagement tiers:
- **Foundation:** monthly retainer, monitoring, drift detection, quarterly capability review. Business-hours support.
- **Production:** Foundation + 24/7 oncall, incident response SLAs, compliance reporting on a quarterly cadence.
- **Strategic:** Production + dedicated technical advisor, monthly executive reviews, custom evaluation work, advance access to platform improvements.
Tiers can be customized; many customers blend (Production for the deployed system, Strategic-level advisory for ongoing feasibility and ROI tracking).
## Compliance reporting
For regulated customers, we deliver:
- Monthly or quarterly compliance reports tied to your audit cadence
- Evidence packages for SOC 2 Type II, HIPAA, GDPR, FERPA, PCI, FINRA where relevant
- Pre-audit readiness reviews ahead of formal audit cycles
- Regulator inquiry support when you need it
The same team that built the system runs it. There's no "throw it over the wall" handoff to a separate operations vendor. When the model drifts, the engineers who built the architecture know how to fix it.
### Private AI Deployment
URL: https://www.mycustomai.io/services/private-ai-deployment
Summary: VPC, on-prem, and air-gapped deployment inside your security boundary. HIPAA/SOC 2 alignment, audit logging, key management. The pattern we've shipped six times.
## What "Private AI Deployment" means
Private AI Deployment is the pattern that takes a working AI system and ships it to production *inside your security boundary*. That means:
- Inference runs on infrastructure under your control (your VPC, your data center, your air-gapped enclave).
- Data — prompts, responses, retrieval context — never traverses a third-party model API.
- Encryption keys are customer-managed.
- Audit logging is yours, retained per your policy.
- The model provider does not see your data.
We've shipped this pattern six times across different regulated industries. Each deployment is customer-specific, but the architecture skeleton is mature, tested, and refined.
## When this is the right engagement
- **You already have a working AI system** (built by us, your team, or another vendor) and need to deploy it inside your security perimeter.
- **You're building a security or compliance product** and need to embed AI in a way that doesn't break your customers' data sovereignty.
- **Your industry doesn't allow** sending data to third-party model APIs (healthcare PHI, legal privilege, classified or sensitive security telemetry, etc.).
- **You're consolidating** multiple business units onto a single AI platform with multi-tenant isolation.
## Deployment shapes we ship
## Architecture pattern
A typical Private AI Deployment includes:
- **Inference plane:** vLLM, TGI, or sglang servers behind your load balancer. GPU autoscaling tuned to your latency and cost targets.
- **Retrieval plane:** vector database (Qdrant, Weaviate, or a managed equivalent) inside your perimeter, with embedding compute also internal.
- **Data plane:** customer-managed KMS, encrypted at rest and in transit, no shared keys across tenants.
- **Network plane:** private subnets, deny-all egress unless explicitly allowed, optional service-mesh for east-west encryption.
- **Compliance plane:** prompt-level audit log, response-level audit log, role-based access at retrieval time, PII redaction guardrails, configurable retention.
Compliance documentation is delivered as part of the engagement, including control mapping for SOC 2, HIPAA, and (where relevant) FedRAMP and ISO 27001.
## Why customers pick us for this
Most AI vendors will sell you a model. Some will sell you an integration. Very few have built and shipped the full multi-tenant private-deployment pattern across regulated verticals. The architecture we deliver is not a first-time build — it's the consolidated lessons from prior engagements.
On industry pages we use the local language: HIPAA-aligned (Healthcare), FERPA-aligned (Education), PCI DSS-aligned (Retail), privilege-preserving (Legal), air-gapped (Cybersecurity). Same underlying capability, framed for the buyer.
### Training & Enablement
URL: https://www.mycustomai.io/services/training-and-enablement
Summary: Executive AI workshops and engineering team upskilling. Anchored by our CEO's invited talks at MIW, Stanford, and Columbia.
## Two distinct offerings, one page
### Executive AI Training
> Taught at top universities. Delivered to your board.
Our CEO is an invited speaker at MIW, Stanford, Columbia, and leading industry conferences. The same material that informs graduate students gets distilled into board-ready workshops for executive teams.
Executive AI Training engagements include:
Sessions are delivered in-person or live virtual. Custom curricula are built per organization based on your industry, regulatory footprint, and current AI maturity.
[Inquire about Executive AI Training →](/schedule-discovery-call)
---
### Engineering Enablement
For engineering teams that will own and extend the AI system after delivery. Tied to *your* deployed system, not generic AI courseware.
Engineering Enablement engagements include:
Cohort sizes are typically 4-12 engineers per session, with 4-week and 6-week formats available. Outcomes are measured against agreed-upon competency rubrics.
## Why this matters
The most successful AI deployments we've seen are the ones where the customer's own team can operate, extend, and evaluate the system on day one of production. The least successful are where the vendor walks away and the team can't change a prompt without raising a ticket.
Training & Enablement is the antidote to that failure mode. By the end of the engagement, your team owns the system.
We mention the speaking engagements at MIW, Stanford, and Columbia because those credentials matter to enterprise buyers vetting executive training engagements. We're explicit about what that translates to operationally: original curriculum, peer-reviewed material, and a teaching practice that holds up to academic scrutiny.
## Industries
### AI for Cybersecurity
URL: https://www.mycustomai.io/industries/cybersecurity
Summary: Air-gapped and on-prem AI for SOCs, MSSPs, and security platforms. Threat triage, alert summarization, and policy automation that runs where you operate.
## Why security teams choose private AI
Security telemetry is sensitive twice over. It contains your customers' data, and it reveals your defensive posture. Sending alerts and indicators to a third-party model API creates two attack surfaces: the data exposure itself, and the model provider's own security posture.
We build AI that runs inside your environment. Air-gapped if you need it. On-prem if you operate that way. VPC if cloud is your default. The model provider does not see your alerts.
## Where we focus
## Why deployment shape matters here
Air-gapped, on-prem, and customer-VPC deployment isn't an aesthetic choice in security — it's the only deployment shape some buyers will accept. We've shipped on all three patterns, and the architecture decisions cascade from there:
- **Air-gapped:** open-weight model, customer-hosted inference, no telemetry, signed model artifacts.
- **On-prem:** dedicated GPU infrastructure, customer-controlled MLOps, private model registry.
- **Customer VPC:** model in your cloud account, customer-managed keys, no egress to vendor SaaS.
## How we engage
Security customers usually start with an **AI Pilot** on a real alert backlog (post-anonymization). From there, **Private AI Deployment** establishes the pattern that your security architecture team can sign off on. **Managed AI** maintains the model against shifting threat landscapes and emerging adversarial techniques.
If you sell a security product, we can embed AI inside your product without forcing your customers to accept a new SaaS data flow. The model deploys with your software; the inference runs where your software runs.
### AI for Education
URL: https://www.mycustomai.io/industries/education
Summary: FERPA-aligned AI for higher education, K-12, and EdTech. Student data stays where it belongs while AI powers learning and operations.
## Why educational institutions choose private AI
Student records are FERPA-protected, and the consequences of a casual API integration include funding-eligibility risk. Beyond compliance, faculty and students need to trust that course content, drafts, and conversations aren't being absorbed into someone else's training pipeline.
Private AI keeps student data in your environment. Models run on infrastructure you control. Faculty intellectual property and student work never train an external model.
## Where we focus
## FERPA posture
- **Student data never leaves your environment.** Inference runs inside your IT footprint.
- **Educational records stay educational records.** No exposure to model providers.
- **Role-based retrieval.** Advisors see advising context; faculty see course context; students see their own context.
- **Configurable retention.** Conversation logs respect your records-retention schedule.
## How we engage
Most institutions start with an **AI Pilot** on a contained use case — a single course, a single department, or a single admin workflow. From there, **Custom AI Build** plus **Private AI Deployment** establish the institutional pattern that scales across departments. **Training & Enablement** is critical here: faculty and staff adoption is the difference between a successful AI program and a shelved one.
Every educational AI we build includes accommodations and accessibility from day one — multi-modal output, plain-language modes, and transparent uncertainty surfacing.
### AI for Financial Services
URL: https://www.mycustomai.io/industries/financial-services
Summary: Risk, compliance, and financial-crime AI for banks, insurers, and fintechs — deployed inside your data boundary, with FINRA-aligned audit logging and data residency.
## Why financial services chooses private AI
Financial-services buyers don't have the option of "we'll figure out the compliance later." Transaction data, customer PII, and material non-public information are regulated assets. Off-the-shelf model APIs — even those marketed as enterprise-grade — create a cross-border data flow, a third-party vendor risk review, and a board-level disclosure question.
We build AI that doesn't create those questions in the first place. The models run in your VPC or on infrastructure you control. The audit log is yours. The residency is yours. The keys are yours.
## Where we focus
## Compliance posture
Every Financial Services engagement ships with:
- SOC 2 Type II-aligned controls (or aligned to your existing program)
- Customer-managed encryption keys (KMS) for at-rest and in-transit data
- Network isolation: deny-all egress, private subnets, no internet path required
- Audit logging at the prompt and response level, retained for your supervisory window
- Optional data residency in EU, UK, US-East, US-West, or your preferred region
## How we engage
Most Financial Services projects begin with an **AI Pilot** — a 4-6 week hands-on feasibility study on a real sample of your data. From there, customers graduate into **Custom AI Build** plus **Private AI Deployment**. Once in production, **Managed AI** keeps the system tuned to changing model and regulatory landscapes.
Multi-tenant isolation per business unit, VPC-pinned inference, customer-managed KMS keys, prompt-level audit logging, and PII redaction guardrails on every input and output. Available as a deployable manifest on engagement kickoff.
### AI for Healthcare
URL: https://www.mycustomai.io/industries/healthcare
Summary: HIPAA-aligned AI for providers, payers, and life sciences. PHI stays in your VPC — clinical and operational copilots ship to production.
## Why healthcare chooses private AI
PHI is regulated, and the regulator is the federal government. A breach is not a marketing issue — it's a HIPAA enforcement action with per-record penalties. Most off-the-shelf model APIs are not HIPAA-aligned out of the box; the ones that are require BAAs, careful prompt review, and ongoing risk assessments.
Private AI side-steps the entire question. PHI never leaves your VPC. The model runs on infrastructure under your covered-entity or business-associate control. The audit log is yours. The keys are yours.
## Where we focus
## HIPAA posture
- **PHI never traverses third-party model APIs.** Inference is in your environment.
- **Customer-managed encryption.** AES-256-GCM at rest with keys under your KMS.
- **Auditable trace.** Prompt, response, model version, and user logged for the full retention period your privacy program requires.
- **Minimum necessary by retrieval.** RAG respects role-based access at retrieval time, not just at display time.
- **De-identification optional.** PII redaction guardrails configurable per workflow.
## How we engage
Healthcare projects begin with an **AI Pilot** scoped to a single workflow — typically clinical documentation, prior auth, or a payer-side claims question. **Custom AI Build** plus **Private AI Deployment** ship the production system inside your VPC. **Training & Enablement** ensures your clinicians and operators know how to use, evaluate, and trust the system.
We do not market clinical decision support without explicit FDA pathway involvement. The systems we build are operational and documentation copilots that augment licensed clinical judgment, not replace it.
### AI for Legal & Compliance
URL: https://www.mycustomai.io/industries/legal-and-compliance
Summary: Privilege-preserving AI for law firms, in-house legal, and compliance teams. Built so attorney work product never leaves your environment.
## Why legal teams choose private AI
Legal work is irreducibly confidential. A privilege waiver from a careless data flow is not a small problem — it's a malpractice problem. When you send matter content through a third-party API, even a "no training on your data" promise, you've created a discovery question that you can't easily answer.
Private AI eliminates that question. Models run in your VPC, on customer-managed keys, with no path to external inference endpoints. Every prompt and response is logged with the matter ID. Privileged content stays where it belongs.
## Where we focus
## Privilege and audit posture
- **Matter-level isolation.** Every project is scoped to a matter or client; cross-tenant retrieval is structurally prevented.
- **Court-defensible audit log.** Every model interaction is logged with timestamp, user, matter, prompt hash, and response. Retention is configurable to your firm's policy.
- **No external inference.** Models run on infrastructure you control. There is no API path off your environment.
- **Privilege screens at retrieval.** RAG pipelines respect privilege flags on documents and never surface restricted content to non-cleared users.
## How we engage
Most law firms and in-house teams start with an **AI Pilot** on a contained matter or contract corpus. From there, **Custom AI Build** integrates with your DMS (iManage, NetDocs, SharePoint) and matter system, and **Private AI Deployment** establishes the infrastructure pattern your security team can sign off on once and reuse.
We are not a law firm and do not provide legal advice. We help your firm or legal department deploy AI inside your boundary. Privilege analysis remains the responsibility of your counsel.
### AI for Retail
URL: https://www.mycustomai.io/industries/retail
Summary: Agentic commerce and PCI-aligned AI for retailers and marketplaces — from product copilots to fraud and recommendations.
## Where retail is going
The agentic commerce shift is real: shoppers are starting to ask agents to *do* things, not just retrieve answers. The retailers and marketplaces that win will be those whose product catalog, merchandising rules, and policies are accessible to agents — without giving every agent provider direct access to customer data.
We've published [open research on agentic e-commerce](/blog/the-agentic-retail-revolution-redefining-e-commerce-in-the-genai-era) and built the architecture pattern across multiple retail customers.
## Where we focus
## Compliance posture for retail
- **PCI DSS-aligned.** Payment data segregated; no payment context in model prompts.
- **Customer PII redacted at the boundary.** Names, addresses, and payment methods replaced with tokens before retrieval.
- **Tenant isolation per business unit.** Multi-banner retailers can isolate banner data while sharing platform infrastructure.
## How we engage
Retailers typically begin with **AI Feasibility & ROI** when the question is portfolio-level: where to deploy AI first across commerce, supply chain, and customer support — and what each path will cost and return. If there's already a specific workflow ready to validate, they start with an **AI Pilot** instead. **Custom AI Build** and **Private AI Deployment** ship the production system, often integrating with existing commerce platforms (Shopify, Salesforce Commerce, custom).
Agentic e-commerce was the original positioning of MyCustomAI. We continue to invest in research and tooling for retail, even as our broader practice has expanded into other regulated industries.
## Technologies
### Agentic AI & MCP Servers
URL: https://www.mycustomai.io/technologies/agentic-ai-mcp
Summary: Production-grade agentic AI with Model Context Protocol (MCP) integration. Tool use, multi-step reasoning, and grounded action — auditable end to end.
## What it is
Agentic AI is AI that *acts* — not just answers. Multi-step workflows where the model selects and invokes tools, reasons about outputs, and takes deliberate action in your systems. MCP (Model Context Protocol) is the emerging open standard for connecting agents to tools and data; we ship MCP-aligned integrations from day one.
## When you'd use it
- **SOC alert triage and enrichment** with tool-mediated investigation
- **Compliance and AML investigation copilots** that pull case context automatically
- **Agentic commerce** for multi-turn shopping and checkout
- **Legal and contract workflows** that operate across DMS, matter system, and email
## Technical depth
## Why this matters
Most production AI failures in 2026 come from poorly-grounded agentic systems. The architectural discipline — eval-first design, clear tool boundaries, audit logging — is what separates a demo from a system you can put in front of regulated customers.
### Computer Vision
URL: https://www.mycustomai.io/technologies/computer-vision
Summary: Custom computer vision models for classification, detection, segmentation, and visual reasoning — deployed at the edge, on-prem, or in your cloud.
## What it is
Computer vision systems for visual understanding tasks — from image classification and object detection to segmentation and visual question answering. Built on open-source backbones (DINOv2, SAM, vision transformers) with custom training on your data.
## When you'd use it
- **Retail catalog enrichment** at SKU scale
- **Quality inspection** in manufacturing and supply chain
- **Medical imaging support** (operational, not clinical decision support)
- **Security and surveillance analytics** in your perimeter
## Technical depth
### Fine-tuning & Evaluation
URL: https://www.mycustomai.io/technologies/fine-tuning
Summary: Custom fine-tuning of open-weight models on your domain data. Includes data prep, training, evaluation harness, and continuous-improvement workflows.
## What it is
Fine-tuning adapts an open-weight base model (Llama, Mistral, Qwen, or similar) to your specific domain. Done right, it improves quality, reduces cost, and creates intellectual property you own — the fine-tuned weights are yours, deployed in your environment.
## When you'd use it
- When prompt engineering and retrieval are not enough to hit your accuracy bar
- When you have domain-specific patterns the base model doesn't capture
- When you need consistent style or output structure that's hard to prompt for
- When you want to reduce inference cost by using a smaller, fine-tuned model instead of a large general-purpose one
## Technical depth
## Why this matters
In regulated industries, fine-tuned models are a strategic asset. They live inside your environment, they capture your accumulated domain expertise, and they're not subject to silent model-provider changes. The IP is yours.
### Multimodal, Audio & Video AI
URL: https://www.mycustomai.io/technologies/multimodal-ai
Summary: AI systems that reason across text, audio, and video. Voice copilots, meeting intelligence, video understanding, and multimodal agents.
## What it is
Multimodal AI systems that work across text, audio, image, and video — built on open-weight multimodal foundations (Qwen-VL, Whisper, custom models) and tuned for production deployment in regulated environments.
## When you'd use it
- **Voice copilots** for clinical documentation, customer support, and field operations
- **Meeting intelligence** with retention and disclosure controls
- **Video understanding** for compliance review, training, and surveillance
- **Multimodal agents** that handle multiple input types in one workflow
## Technical depth
### OCR & Document Understanding
URL: https://www.mycustomai.io/technologies/ocr-document-ai
Summary: Scaled OCR + vision-language pipelines for document compliance, archival, claims processing, and contract analysis. Works on print, handwriting, and degraded scans.
## What it is
Document AI combines optical character recognition, layout understanding, and vision-language models to extract structured data from documents at scale. Unlike single-stage OCR, our pipelines route work across model tiers based on page complexity, optimizing both quality and cost.
## When you'd use it
- **Claims and prior auth processing** in healthcare and insurance
- **Contract analysis** for legal and procurement teams
- **Loan document automation** in financial services
- **Archival and compliance digitization** at multi-million-page scale
## Technical depth
## Why this matters
Customers who process documents at scale often start with a generic vision model and hit accuracy or cost walls. Custom-trained pipelines on customer-domain documents typically deliver 5-10x cost reduction at the same quality bar.
### Private LLMs & RAG AI Agents
URL: https://www.mycustomai.io/technologies/private-llm-ai-agents
Summary: Custom LLM AI Agents and RAG systems deployed inside your security boundary. No data leaves your VPC. Open-weight or licensed models.
## What it is
Private LLM AI Agents are conversational AI systems built on open-weight or licensed models, deployed entirely within the customer's environment. Retrieval-augmented generation (RAG) grounds responses in your knowledge base, your documents, your data — without exposing that data to a third-party API.
## When you'd use it
- **Customer support deflection** with order-aware or account-aware retrieval
- **Internal copilots** for compliance, legal, finance, HR, and operations
- **Domain expert assistants** trained on your documentation, runbooks, or research
- **Agentic workflows** where the LLM both answers and takes action
## Technical depth
The architecture pattern we ship combines:
## Why this matters for regulated industries
Off-the-shelf model APIs are not compatible with HIPAA, FERPA, attorney-client privilege, or air-gapped security operations. Private deployment isn't a nice-to-have; it's the only deployment shape some customers will accept.
### Speech AI (ASR & TTS)
URL: https://www.mycustomai.io/technologies/speech-ai
Summary: Speech recognition and text-to-speech for production deployments. Whisper-class ASR with domain adaptation, neural TTS with voice cloning controls.
## What it is
Speech AI systems for both directions: automatic speech recognition (ASR) for voice-to-text, and text-to-speech (TTS) for voice generation. Deployed inside your environment so voice content stays where it belongs.
## When you'd use it
- **Clinical documentation** ambient capture and transcription
- **Customer support** voice channel transcription and quality monitoring
- **Compliance surveillance** of recorded calls and meetings
- **Accessible interfaces** with TTS output for accommodations
## Technical depth
### Inference Infrastructure
URL: https://www.mycustomai.io/technologies/inference-infra
Summary: Production-grade inference infrastructure for open-weight models. vLLM, sglang, TGI, GPU autoscaling, cost and latency tuning.
## What it is
Inference infrastructure is the runtime that serves model predictions. Done well, it hits your latency and cost targets at production scale. Done poorly, it bottlenecks the whole system. We deploy and tune inference for every customer environment we ship to.
## What we deliver
## Why this matters
Open-weight models have closed the capability gap with closed-source models — but only if the inference infrastructure is competently deployed. The cost difference between a naive deployment and a tuned one is often 5-10x.
### Multi-tenant Isolation
URL: https://www.mycustomai.io/technologies/multi-tenant
Summary: Multi-tenant AI deployments where customer data is structurally isolated. Shared infrastructure, isolated data, isolated keys, isolated audit logs.
## What it is
Multi-tenant AI architecture allows a single platform to serve multiple customers with hardware and operational efficiency *while* keeping each customer's data, keys, retrieval indexes, and audit logs strictly isolated. Done right, no customer can ever see another customer's data — by architecture, not by hope.
## What we deliver
## Why this matters
Multi-tenant isolation is the architectural pattern we've shipped six times across regulated customers. It's the same pattern whether the buyer is a software vendor selling to banks, a security vendor selling to enterprises, or a legal AI company selling to firms.
### Observability & Evals
URL: https://www.mycustomai.io/technologies/observability-evals
Summary: Production observability for AI systems: traces, metrics, evaluation harnesses, drift detection, and continuous improvement loops.
## What it is
Observability for AI is more than logging and dashboards. It includes evaluation harnesses that run on every deployment, drift detection on retrieval and generation, user-feedback loops, and the continuous-improvement workflow that keeps a system better six months in than at launch.
## What we deliver
## Why this matters
Production AI is not a "ship and forget" system. The customers we serve longest are the ones whose systems get measurably better over time — and that requires the observability and eval foundation to be there from day one.
### On-prem & Air-gapped AI
URL: https://www.mycustomai.io/technologies/on-prem-air-gapped
Summary: On-prem AI for environments where cloud is not an option. Air-gapped deployments for defense, classified, and high-security customers.
## What it is
On-prem deployments run on customer-owned infrastructure in customer-owned data centers. Air-gapped deployments go further: the AI system has no network path to the public internet, including for model updates and telemetry. Used by defense, classified-environment customers, and the most security-conscious enterprises.
## What we deliver
## Why this matters
Designing for air-gap is the deployment-shape stress test. If the architecture works air-gapped, every other deployment shape (VPC, on-prem networked, edge) becomes trivially easier. We've built for air-gapped customers in cybersecurity and the resulting discipline benefits every other deployment.
### Vector Databases & Retrieval
URL: https://www.mycustomai.io/technologies/vector-databases
Summary: Production retrieval architecture: vector DBs, hybrid search, reranking, and freshness policies. The data plane behind every RAG system we ship.
## What it is
The retrieval plane is the unglamorous foundation under every well-functioning RAG system. We build production retrieval that combines vector search, lexical search, and reranking — tuned per use case, deployed inside customer infrastructure, and observable end to end.
## What we deliver
## Why this matters
Most RAG performance issues are retrieval issues, not generation issues. Investing in the retrieval architecture pays back across every downstream model interaction.
### VPC & Private Cloud Deployment
URL: https://www.mycustomai.io/technologies/vpc-private-cloud
Summary: AI deployed inside your AWS, GCP, or Azure VPC. Customer-managed keys, customer-controlled networking, no path off your environment.
## What it is
VPC deployment means inference, retrieval, and data plane all run inside the customer's virtual private cloud (AWS, GCP, Azure, or equivalent). Encryption keys are customer-managed via KMS. Network paths are customer-controlled. The model provider has no access to anything.
## What we deliver
## Why this matters
VPC is the most common deployment shape for regulated cloud-native customers. Once shipped correctly, the architecture pattern is reusable across new customers and new use cases — which is exactly why we've shipped six iterations of it.
### Audit Logging & RBAC
URL: https://www.mycustomai.io/technologies/audit-rbac
Summary: Prompt-level audit logging and role-based access control across every AI workflow. Court-defensible, examiner-defensible, configurable retention.
## What it is
Audit logging captures every prompt, response, model version, retrieved context, and user action. RBAC ensures users only see and act on data they're authorized for — at retrieval time, not just at display time. Together these two controls underpin every regulator and auditor conversation.
## What we deliver
## Why this matters
In regulated industries, "we have audit logs" is not a checkbox — it's the difference between an answerable regulator inquiry and an open-ended investigation. The logs need to be tamper-evident, retained for the right period, and queryable.
### GDPR & Data Residency
URL: https://www.mycustomai.io/technologies/gdpr-data-residency
Summary: EU-residency AI deployments and GDPR-aligned data flows. Data subject rights, lawful basis, and cross-border transfer constraints handled by design.
## What it is
GDPR and data-residency deployments keep EU personal data inside EU infrastructure (Frankfurt, Dublin, Paris, etc.) with no cross-border transfer to model providers or vendor SaaS. Data subject rights — access, rectification, erasure — are designed into the system, not bolted on.
## What we deliver
## Why this matters
European customers — and US customers serving European users — cannot use AI systems whose inference flows traverse non-EU regions. Building for EU residency from the start avoids the painful retrofit when the first European customer asks.
### HIPAA-aligned AI Deployments
URL: https://www.mycustomai.io/technologies/hipaa
Summary: HIPAA-compliant AI for healthcare providers, payers, and life sciences. PHI stays in your VPC. BAA-eligible infrastructure, audit logging, customer-managed encryption.
## What it is
HIPAA-aligned AI deployments are AI systems architected to meet HIPAA Privacy and Security Rule requirements: PHI never traverses third-party model APIs, encryption is customer-managed, audit logging is comprehensive, and the infrastructure operates under a BAA when third-party services are involved.
## What we deliver
## Why this matters
HIPAA enforcement has teeth: per-record penalties scale fast. The architectural choice to keep PHI inside the customer's environment isn't extra effort — it's the only deployment shape that doesn't require a multi-month risk assessment cycle for every new use case.
### PII Redaction & Guardrails
URL: https://www.mycustomai.io/technologies/pii-guardrails
Summary: Automated PII detection, redaction, and policy enforcement at every stage of the AI pipeline — input, retrieval, output.
## What it is
PII guardrails are policy-enforced redaction and validation that operate at every stage of an AI pipeline: input sanitization, retrieval-time access control, output filtering. The goal is structural: a system where PII *cannot* leak even when a prompt or model behaves unexpectedly.
## What we deliver
## Why this matters
Prompt injection and model unpredictability are real. Defense in depth means PII never reaches the model in raw form, never gets retrieved without authorization, and never escapes the output channel without filtering.
### SOC 2 Type II Readiness
URL: https://www.mycustomai.io/technologies/soc2
Summary: AI systems built and operated to meet SOC 2 Type II controls. Evidence packages, control mapping, audit support.
## What it is
SOC 2 Type II is the de facto enterprise security baseline for SaaS and managed-services vendors. Every AI deployment we ship is built and operated to meet SOC 2 Type II controls — Security at minimum, with Availability, Confidentiality, Processing Integrity, and Privacy added per customer requirement.
## What we deliver
## Why this matters
If your AI vendor's deployment isn't SOC 2-aligned, your enterprise customers will discover it during their vendor-risk review. The cleanest path is to build with SOC 2 alignment from day one rather than retrofit.
## Blog
### Natural-Language Interfaces for the Software You Own
URL: https://www.mycustomai.io/blog/natural-language-interfaces-for-the-software-you-own
Summary: Natural-language-to-use (NL-to-use) lets teams ask for outcomes in plain English while the AI safely invokes the software they already own—APIs, tools, and repos—under explicit contracts and tests. With typed tool calling, shared standards (OpenAPI/JSON Schema), and execution-based verification, leaders can track reliability via ECR/TPR, control cost-of-pass, and scale from demos to dependable operations across dev, ops, data, support, and marketing.
## 1\. Introduction
Imagine saying, “Extract tables from these PDFs and load them into our dashboard,” or “Clean this repo and generate a styled image,” and having it done by the systems you already use—no new app or brittle glue code. That’s **natural-language-to-use (NL-to-use)**: you state a goal in plain language, and an AI translates it into governed, auditable calls to your existing APIs, apps, and repositories. The output isn’t “some code you still have to wire up”—it’s a **finished, verifiable outcome**.
Leaders care because outcomes become consistent and measurable. Two simple, board-friendly metrics make progress visible: **ECR** (did the run finish?) and **TPR** (did it pass predefined checks?). When these rise—and cost per successful run falls—you’re compounding ROI, not just shipping a demo.
## 2\. Background
#### 2.1 What NL-to-use means in practice
To make the idea concrete, here are the four building blocks that show up in every successful deployment:
- **Intent capture:** First, disambiguate the request and collect required inputs. Modern model APIs support typed, schema-validated tool calls so arguments are precise and checkable.
- **Grounding:** Next, map the request to documented capabilities—APIs, CLIs, apps, repos—using machine-readable contracts like OpenAPI and JSON Schema.
- **Execution:** Then orchestrate real calls in real environments, handling credentials, dependencies, and side effects. This goes beyond chat; it’s reliable operation of live systems.
- **Verification & provenance:** Finally, confirm success against predefined checks and emit an auditable trace of calls, IO, and decisions (e.g., via GenAI spans).
#### 2.2 How it differs from adjacent ideas
Because terms get conflated, it helps to separate NL-to-use from neighboring approaches:
- **Not NL-to-code:** Codegen emits new code; NL-to-use primarily **invokes** existing capabilities and treats any emitted code as temporary scaffolding.
- **Not just AI Agents:** The focus is executing actions under explicit contracts with measurable outcomes, not conversation alone.
- **Not classic RPA:** RPA replays screens and clicks; NL-to-use prefers typed interfaces with verifiable results. GUI control is a last-resort tool—still under guardrails.
#### 2.3 Why now
Momentum is real because several foundations have clicked into place:
- **Common contracts:** JSON Schema and OpenAPI 3.1 make API surfaces portable and typed; model platforms now support structured function/tool calling to reduce ambiguity.
- **Interop protocols:** MCP standardizes how agents connect to tools/data; A2A standardizes how agents discover and collaborate via simple “agent cards.”
- **Execution-based evaluation:** Benchmarks like WebArena, OSWorld, and GitTaskBench test whether tasks complete and pass checks—moving from “sounds good” to “works.”
- **Observability & governance:** OpenTelemetry GenAI spans, supply-chain attestations, sandboxing, and dependency pinning make execution safer in production.
#### 2.4 A recent development: “Agentized” repositories
One practical pattern turns GitHub repositories into interactive **agents** that you can call in natural language:
- **Setup:** Read docs, plan a structured TODO, install dependencies, fetch models/data, and prepare validation samples.
- **Use:** Create a repo-specific agent that executes tasks from plain English, retrying and revising plans on errors.
- **Collaborate:** Publish “agent cards” so repo-agents can chain capabilities through an A2A protocol.
Why this matters: it showcases the shift from “AI that writes code” to **“AI that uses your code and tools”**—with validation-first setup and reliability you can measure (ECR/TPR).
## 3\. Business Applications
Organizations that layer NL-to-use onto systems they already license report faster cycles and fewer bespoke integrations. Below are illustrative domains to make the value tangible:
- **Developer acceleration:** In IDEs and platforms, assistants handle authoring, refactoring, reviews, and repo Q&A—speeding completion times and increasing throughput, especially for juniors.
- **IT/DevOps & service operations:** Summaries, grounded answers, and guided remediation reduce handle time and escalations; AIOps flows improve MTTR while keeping actions auditable.
- **Data & document workflows:** Natural-language over contracts/emails compresses triage and review; governed connectors keep sensitive data in-platform.
- **Customer operations:** Assist agents and deflect chats using CRM/KB context; large field studies show higher issues-resolved-per-hour, with outsized gains for novices.
- **Analytics & governed data access:** NL query/notebook assistance within data platforms saves hundreds of hours without punching holes in governance.
- **Creative & marketing production:** Asset generation and adaptation shift from days to hours, increasing variants while lowering external spend.
To run this like an operation—not a demo—track a small set of metrics:
- **ECR:** Percent of runs that complete without tool/model errors.
- **TPR:** Percent of runs that pass predefined checks.
- **Latency & p95 runtime:** Impact on time-to-resolution and SLAs.
- **Cost-of-pass:** Spend per successful run—critical for multi-step workflows.
## 4\. Future Implications
The next 12–36 months will favor teams that standardize skills and make success observable:
- **Standard skills & internal marketplaces:** Expect shared skill schemas (“agent cards”) and A2A to become vendor-neutral interfaces; enterprises will curate marketplaces with versioning, SLAs, and provenance.
- **Verification as table stakes:** Validation datasets, contract tests, and end-to-end auditing will be required, with benchmarks expanding to multi-repo, long-horizon tasks that also track cost/latency.
- **Hardened execution & governance:** Micro-VM sandboxing by default, dependency pinning, supply-chain attestations, and least-privilege access will be baseline expectations.
- **Measurable operations:** ECR, TPR, p95, and cost/run will drive budgeting and SLOs—managed via SRE-style error budgets.
- **Evolving roles:** Developers curate skills and validations (instead of writing glue code); Ops runs the auditable control plane; business owners set outcomes, budgets, and guardrails.
Leaders should also keep a few questions front-of-mind:
- **Standards & trust:** How will A2A, MCP, and platform manifests converge—and what verification tiers/SLAs will your marketplace require?
- **Risk & accountability:** When workflows compose multiple skills/agents, who owns the outcome—and how does provenance resolve incidents?
- **Long-horizon reliability & cost:** As tasks span more steps/systems, how will you keep TPR high and cost-of-pass low without constant human oversight?
## 5\. References
#### 5.1 Core standards & protocols
- [Model Context Protocol (MCP) — Draft Spec](https://spec.modelcontextprotocol.io/specification/draft/)
- [JSON Schema — Spec (2020-12)](https://json-schema.org/specification)
- [OpenAPI Specification v3.1.x](https://spec.openapis.org/oas/v3.1.0.html)
- [OpenRPC Specification 1.3.2](https://spec.open-rpc.org/)
- [JSON-RPC 2.0 Specification](https://www.jsonrpc.org/specification)
- [Agent-to-Agent (A2A) Protocol — Overview](https://a2a-protocol.org/latest/topics/what-is-a2a/)
#### 5.2 Tool use & platform mechanics
- [OpenAI — Structured Outputs](https://openai.com/index/introducing-structured-outputs-in-the-api/)
- [Anthropic — Implement Tool Use](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/implement-tool-use)
- [Vertex AI — Function Calling](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling)
- [OpenTelemetry — GenAI Spans](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/)
#### 5.3 Benchmarks & methods
- [GitTaskBench (2025)](https://arxiv.org/abs/2508.18993)
- [WebArena](https://arxiv.org/abs/2307.13854)
- [OSWorld](https://arxiv.org/abs/2404.07972)
- [ReAct](https://arxiv.org/abs/2210.03629)
- [Gorilla](https://arxiv.org/abs/2305.15334)
- [SayCan](https://arxiv.org/abs/2204.01691)
#### 5.4 Context & adjacent work
- [SWE-bench Verified](https://openai.com/index/introducing-swe-bench-verified/)
- [OpenHands](https://arxiv.org/abs/2407.16741)
- [SWE-agent](https://arxiv.org/abs/2405.15793)
- [Task-oriented Dialogue Surveys](https://arxiv.org/abs/2311.09008)
- [IEEE SA 2755.1-2019 — IPA Taxonomy](https://standards.ieee.org/standard/2755_1-2019.html)
#### 5.5 Business evidence & platform examples
- [RCT: Impact of AI on Dev Productivity](https://ar5iv.org/pdf/2302.06590)
- [Multi-company Field Experiments](https://www.nber.org/system/files/working_papers/w31161/revisions/w31161.rev0.pdf)
- [GitHub — Code Quality & Reviews](https://github.blog/2023-10-10-research-quantifying-github-copilots-impact-on-code-quality/)
- [Amazon Q — Developer Hours Saved](https://aws.amazon.com/blogs/devops/reducing-time-spent-waiting-with-amazon-q/)
- [ServiceNow — Agent Productivity](https://www.servicenow.com/blogs/2024/support-agent-productivity-genai)
- [Dynatrace — Faster MTTR](https://www.dynatrace.com/news/blog/advancing-aiops-preventive-operations-powered-by-davis-ai/)
- [Security Copilot RCT](https://ar5iv.org/pdf/2411.01067)
- [EY — Compliance Search Reduction](https://www.ey.com/en_us/insights/consulting/natural-language-processing-revs-up-manual-search-time)
- [UiPath — Hiscox Case Study](https://www.uipath.com/resources/automation-case-studies/hiscox-achieves-sustainable-growth-with-communications-automation)
- [Klarna — AI Assistant Results](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/)
- [Klarna — Profit/Opex Impact](https://www.klarna.com/international/regulatory-news/klarna-announces-profitable-start-to-2024-as-it-sets-the-stage-for-innovation-and-growth/)
- [Databricks — LakehouseIQ](https://www.databricks.com/blog/introducing-lakehouseiq-ai-powered-engine-uniquely-understands-your-business)
- [Snowflake — Copilot Docs](https://docs.snowflake.com/en/user-guide/snowflake-copilot)
- [Forrester TEI — Adobe Firefly](https://business.adobe.com/content/dam/dx/us/en/resources/reports/forrester-tei-adobe-creative-solutions-for-enterprise/creative-solutions-for-enterprise-powered-by-firefly-generative-ai.pdf)
### Document AI Guide: From PDF/Scan to Reliable Extracted Data
URL: https://www.mycustomai.io/blog/document-ai-guide-from-pdf-scan-to-reliable-extracted-data
Summary: Document AI converts messy PDFs and scans into reliable, auditable data—speeding closes, reducing manual work, and unlocking analytics. This guide explains what Document AI is (and isn’t), compares modular pipelines with end-to-end models, shows where value lands in operations and knowledge workflows, and outlines a pragmatic, hybrid roadmap for the next 2–3 years.
## 1\. Introduction
If your quarterly close stalls because 4,000 invoices need verification—or teams copy numbers from charts in 200-page reports—you’re not alone. Most organizations sit on mountains of PDFs and scans whose structure software can’t see.
**Document AI** changes that. It reads born-digital and scanned documents, reconstructing content _and_ structure—text, layout/reading order, tables and key–values, figures/charts, even math—and emits machine-readable outputs (JSON, HTML/Markdown/LaTeX, or database-ready schemas) with page coordinates and confidence for auditing.
## 2\. Background
### 2.1 What Document AI is—and isn’t
To set expectations, let’s anchor terminology:
- **What it is**: A layout-aware extraction capability that fuses pixels, text, and geometry to recover structure and meaning, emitting grounded outputs your systems can trust and audit.
- **What it isn’t**:
- Plain OCR (characters/words only, no relationships/tables/multi-page hierarchy).
- Generic NLP on clean text (ignores layout cues like position, fonts, reading order).
- An IDP platform (that’s workflow orchestration; Document AI is the extraction engine inside).
Standards such as **Tagged PDF (ISO 32000-2)** and **PDF/UA** make document structure first-class; **ALTO XML** shows how text, geometry, reading order, and confidence can be stored for auditability.
### 2.2 Two approaches shaping the field
When choosing an approach, consider variability, precision, and cost:
- **(1) Modular pipelines**: Configurable chains (layout → OCR → table/chart/math parsers) with rules.
- _Strengths_: Tunable per format, traceable, predictable cost/latency; great on dense, precision-critical layouts.
- _Limits_: Maintenance across formats, error propagation, limited “global” reasoning.
- **(2) End-to-end VLMs**: Pages in, structured output (often JSON/Markdown) out—sometimes OCR-free.
- _Strengths_: Global understanding, simpler integration, flexible prompting/schemas.
- _Limits_: Higher compute/latency; brittle on very dense text and complex, multi-page tables; multilingual/handwriting vary.
**Bottom line**: End-to-end is rising fast; modular still wins where exactness is non-negotiable.
### 2.3 A plain-English example
To make this concrete, imagine a scanned invoice:
- **Input**: Header, vendor address, invoice date, a 150-row line-item table, and a tax summary.
- **Output**: Header fields with coordinates and confidence; a normalized table preserving merged cells and numeric types; reading order/section labels; and delivery as JSON/CSV for ERP plus HTML/Markdown for review.
### 2.4 How progress is measured (in practice)
Before buying or tuning, insist on measurable quality. Typical KPIs include:
- **Layout detection**: Precision/recall for blocks like table/figure/paragraph.
- **Reading order**: Sequence similarity to human reading.
- **OCR/text**: Character/word error rates (edit distance).
- **Tables**: **TEDS** for structure fidelity (more informative than cell matching).
- **Math**: Exact match & edit distance; **CDM** accounts for multiple valid LaTeX renderings.
- **Charts**: RNSS/RMS for number and mapping accuracy.
- **End-to-end**: Composite page/document metrics.
Benchmarks such as **PubLayNet**, **PubTabNet/PubTables-1M**, **DocVQA**, and **OmniDocBench** enable fair comparisons and track improvements over time.
### 2.5 Where techniques stand today (high-level)
To orient your roadmap, here’s the current snapshot:
- **Layout**: Transformer-based detectors are strong; semantics boost reading order on complex pages.
- **OCR**: Major gains; dense pages & unusual fonts still challenge accuracy.
- **Tables**: Detection is mature; structure recognition remains hard for merged, borderless, or multi-page cases (image-to-sequence helps).
- **Math**: Improving, but still brittle for production robustness; CDM is a step forward.
- **Charts**: Classification is solid; consistent raw-data extraction is early.
- **Large document models**: Donut, LayoutLMv3, Pix2Struct, Nougat, and OmniDocBench signal a shift to end-to-end outputs and standardized evaluation.
**Decision-maker takeaway**: Choose by **variability** and **precision needs**—modular for stable, compliance-grade formats; end-to-end for coverage and speed; hybrids for both.
## 3\. Business Applications
Document AI’s ROI shows up along two pillars. Think of these as building blocks you can combine.
### 3.1 Operational automation & compliance
These are high-volume, rule-bound workflows where accuracy and auditability matter:
- **Accounts payable & expenses** (invoices, receipts, POs): Higher STP, faster close, fewer exceptions via OCR + layout + table structure; TEDS drives measurable tuning.
- **Insurance claims & healthcare RCM** (claims, EOB/EOP, charts): Faster adjudication, fewer touches, better coding with form understanding and table parsing mapped to standards (e.g., FHIR).
- **Banking & lending** (KYC/AML, mortgages/loans): Shorter time-to-decision and improved compliance using packet assembly, reading order, and traceable extraction from dense statements/paystubs.
- **Logistics & trade** (BOL, customs, certificates): Faster clearances and accurate landed costs via multilingual OCR and robust table extraction across pages.
- **Contracts & procurement** (MSAs, NDAs, SOWs): Quicker intake and obligation tracking using clause detection and structured outputs for CLM/ERP.
- **Regulatory & financial reporting** (filings, disclosures): Reduced manual effort and errors by mapping PDFs to structured taxonomies (e.g., iXBRL) with schema validation.
**What to measure**: STP rate, exception/rework rate, human minutes per page, **TEDS** for tables, **CER/WER** for OCR, and latency/cost per 1k pages.
### 3.2 Knowledge, analytics, & productization
These use cases monetize unstructured PDFs and improve decision-making:
- **Enterprise search & RAG**: Better answers when indices include tables, charts, figure captions, and multi-page structure—not just text.
- **Analyst workflows & surveillance** (financial/ESG/risk): Scalable monitoring with standardized KPIs, provenance, and citations.
- **Scientific & technical intelligence**: Turn PDF-only knowledge (experimental tables, plot data, math) into analyzable datasets using table, chart-to-table, and formula extraction.
- **Data products from PDFs at scale**: Create revenue from structured datasets with pipelines that enforce precision/recall, TEDS, edit distance, and human sampling SLAs.
**Signals for approach selection**:
- **Stable, compliance formats** → Modular pipelines + strong table/OCR + human review.
- **Mixed, complex corpora** → Pilot end-to-end page-to-JSON/Markdown; route tricky pages (dense tables, math, complex charts) to specialists.
- **Global footprint** → Verify multilingual/script coverage; performance varies by language, font, and layout.
## 4\. Future Implications
The near-term path is pragmatic: combine global reasoning with specialist precision. Here’s what to expect in the next 2–3 years:
- **Hybrid dominance**: Large models guide structure/reading order; specialists handle complex tables, math, and charts—balancing accuracy, speed, and cost.
- **Closing the precision gap**: With broader datasets and better training, end-to-end models approach specialist accuracy—especially on multi-page hierarchy.
- **Efficiency unlocks adoption**: Smaller/faster architectures, visual-token compression, and modern decoding/serving reduce GPU cost/latency and enable VPC/on-prem deployments.
- **Standardized, fine-grained evaluation**: Wider use of TEDS (tables), CDM (math), RNSS/RMS (charts), and composite end-to-end metrics for transparent vendor reporting.
- **Downstream lift**: Better parsing boosts RAG quality, compliance workflows, and analytics; reliable table/chart/math extraction will recover raw data from PDFs at scale.
- **Trust & governance**: Expect grounded outputs (coords + confidences) with retained eval logs, aligning with emerging risk-management practices and regulations.
**Open questions**:
- When do complex tables/charts/math become “boringly reliable” across languages and noisy scans—and what benchmarks prove it?
- Which hybrid patterns (retrieval-then-reason, model-guided routing, schema-constrained decoding) maximize ROI under latency/budget constraints?
- How quickly will structure-first, multi-task benchmarks (e.g., OmniDocBench) become the lingua franca beyond QA?
## 5\. References
#### 5.1 Core surveys, standards, and platform references
- \[A\] Q. Zhang et al. _Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction_. arXiv:2410.21169v4 — [http://arxiv.org/abs/2410.21169v4](http://arxiv.org/abs/2410.21169v4)
- \[1\] _Document AI: Benchmarks, Models and Applications_ — [https://arxiv.org/abs/2111.08609](https://arxiv.org/abs/2111.08609)
- \[2\] _LayoutLM_ — [https://arxiv.org/abs/1912.13318](https://arxiv.org/abs/1912.13318)
- \[3\] _LayoutLMv3_ — [https://ar5iv.org/abs/2204.08387](https://ar5iv.org/abs/2204.08387)
- \[4\] _Donut_ — [https://arxiv.org/abs/2111.15664](https://arxiv.org/abs/2111.15664)
- \[5\] _Pix2Struct_ — [https://arxiv.org/html/2210.03347](https://arxiv.org/html/2210.03347)
- \[6\] Google Cloud — Document AI overview — [https://docs.cloud.google.com/document-ai/docs/overview](https://docs.cloud.google.com/document-ai/docs/overview)
- \[7\] Google Cloud — Document resource (canonical JSON) — [https://cloud.google.com/document-ai/docs/reference/rest/v1beta3/Document/](https://cloud.google.com/document-ai/docs/reference/rest/v1beta3/Document/)
- \[8\] AWS Textract — AnalyzeDocument — [https://docs.aws.amazon.com/textract/latest/dg/API\_AnalyzeDocument.html](https://docs.aws.amazon.com/textract/latest/dg/API_AnalyzeDocument.html)
- \[9\] Everest Group — IDP definition — [https://www2.everestgrp.com/reports/EGR-2021-38-R-4432](https://www2.everestgrp.com/reports/EGR-2021-38-R-4432)
- \[10\] ISG — IDP market definition — [https://www.businesswire.com/news/home/20220222005514/en/ABBYY-Named-2021-Leader-in-Intelligent-Document-Processing-by-ISG-and-Quadrant-Knowledge-Solutions](https://www.businesswire.com/news/home/20220222005514/en/ABBYY-Named-2021-Leader-in-Intelligent-Document-Processing-by-ISG-and-Quadrant-Knowledge-Solutions)
- \[11\] PDF Association — Techniques for Accessible PDF — [https://pdfa.org/pdf-accessibility-techniques/](https://pdfa.org/pdf-accessibility-techniques/)
- \[12\] PDF Association — Glossary (Tagged PDF; ISO 32000-2) — [https://pdfa.org/glossary-of-pdf-terms/](https://pdfa.org/glossary-of-pdf-terms/)
- \[13\] ISO 32000-2:2020 overview — [https://pdfa.org/resource/iso-32000-2/](https://pdfa.org/resource/iso-32000-2/)
- \[14\] Wikipedia — PDF/UA (ISO 14289) — [https://en.wikipedia.org/wiki/PDF/UA](https://en.wikipedia.org/wiki/PDF/UA)
- \[15\] Library of Congress — ALTO schema — [https://www.loc.gov/standards/alto/](https://www.loc.gov/standards/alto/)
#### 5.2 Datasets, metrics, and benchmarks
- \[16\] OmniDocBench — [https://github.com/opendatalab/OmniDocBench](https://github.com/opendatalab/OmniDocBench)
- \[17\] PubLayNet — [https://www.researchgate.net/publication/335318873\_PubLayNet\_largest\_dataset\_ever\_for\_document\_layout\_analysis](https://www.researchgate.net/publication/335318873_PubLayNet_largest_dataset_ever_for_document_layout_analysis)
- \[18\] ICDAR 2017 Page Object Detection — [https://cndplab-founder.github.io/ICDAR2017\_POD/results.html](https://cndplab-founder.github.io/ICDAR2017_POD/results.html)
- \[19\] OCR-D eval spec (CER/WER) — [https://ocr-d.de/en/spec/ocrd\_eval.html](https://ocr-d.de/en/spec/ocrd_eval.html)
- \[20\] ICDAR 2015 Text Reading in the Wild — [https://www.researchgate.net/publication/278048566\_ICDAR\_2015\_Text\_Reading\_in\_the\_Wild\_Competition](https://www.researchgate.net/publication/278048566_ICDAR_2015_Text_Reading_in_the_Wild_Competition)
- \[21\] ICDAR 2024 Reading Order metric — [https://link.springer.com/chapter/10.1007/978-3-031-70552-6\_25](https://link.springer.com/chapter/10.1007/978-3-031-70552-6_25)
- \[22\] PubTabNet (introduces TEDS) — [https://ar5iv.labs.arxiv.org/html/1911.10683](https://ar5iv.labs.arxiv.org/html/1911.10683)
- \[23\] PubTables-1M — [https://openaccess.thecvf.com/content/CVPR2022/papers/Smock\_PubTables-1M\_Towards\_Comprehensive\_Table\_Extraction\_From\_Unstructured\_Documents\_CVPR\_2022\_paper.pdf](https://openaccess.thecvf.com/content/CVPR2022/papers/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.pdf)
- \[24\] TableBank — [https://ar5iv.labs.arxiv.org/html/1903.01949](https://ar5iv.labs.arxiv.org/html/1903.01949)
- \[25\] CROHME 2023 overview — [https://dl.acm.org/doi/abs/10.1007/978-3-031-41679-8\_33](https://dl.acm.org/doi/abs/10.1007/978-3-031-41679-8_33)
- \[26\] CDM: Character Detection Matching — [https://arxiv.org/abs/2409.03643](https://arxiv.org/abs/2409.03643)
- \[27\] DePlot: Chart-to-table (RMS/RNSS) — [https://ar5iv.labs.arxiv.org/html/2212.10505](https://ar5iv.labs.arxiv.org/html/2212.10505)
#### 5.3 Tools and exemplars
- \[28\] Marker (PDF/images → Markdown/JSON/HTML) — [https://github.com/datalab-to/marker](https://github.com/datalab-to/marker)
- \[29\] Nougat (Neural Optical Understanding) — [https://github.com/facebookresearch/nougat](https://github.com/facebookresearch/nougat)
### Edge AI, Explained: Why Decisions Are Moving to the Device—and What Comes Next
URL: https://www.mycustomai.io/blog/edge-ai-explained-why-decisions-are-moving-to-the-device--and-what-comes-next
Summary: Edge AI is transforming how businesses deliver intelligence—moving decisions from the cloud to the device for faster speed, stronger privacy, and lower costs. This blog explains what Edge AI is, why it’s gaining momentum, where it’s already creating business value, and what leaders should expect in the next 3–5 years.
## 1\. Introduction
If you could speed up every user interaction, keep sensitive data on the device, and cut network and cloud bills—would you? That’s the promise of **Edge AI**.
Edge AI means running intelligence directly on the device—smartphones, wearables, cameras, vehicles, AR/VR headsets—so tasks complete locally, near the data source. The results: predictable low latency, privacy by design, and resilience when connectivity is weak or unavailable.
Most training still happens in data centers. Devices focus on inference (making predictions) and, increasingly, light personalization.
## 2\. Background: What Edge AI Is—and Why It’s Rising
To make sense of why Edge AI matters, let’s start with a few plain-English definitions:
- **On-device AI**: Runs on a device’s CPU, GPU, or neural engine; keeps data local; works offline.
- **Near-edge AI**: Runs nearby (e.g., gateway or telecom edge node), cutting latency but not fully local.
- **Inference vs. training**: Training = large data centers. Inference = increasingly on devices, with personalization and federated learning.
So, why is this shift happening now? Four forces are driving adoption:
- **Better experiences**: Instant, reliable speech and camera features are possible when you remove the network round trip.
- **Privacy & compliance**: Local data supports GDPR-style minimization and reduces exposure.
- **Economics**: Cloud inference and bandwidth are expensive; local compute keeps costs predictable.
- **Reliability**: Edge keeps features working offline and during outages.
Of course, none of this would be possible without technical breakthroughs. Three key enablers stand out:
- **Efficient model design**: Quantization, pruning, and distillation shrink models to fit within device power and memory budgets.
- **Mobile-ready architectures**: Families like MobileNetV2/V3 and EfficientNet make accuracy and efficiency achievable together.
- **Hardware/software support**: Core ML, TensorFlow Lite, ONNX Runtime Mobile, and NPUs make deployment practical across devices.
It’s no coincidence that speech and vision led the way. These use cases rose first because:
- **Low-latency demand**: Sensor-to-compute loops need millisecond-level responses for natural user experience.
- **Architecture fit**: Speech and vision models benefit from efficient architectures and integer inference.
- **Mature ecosystem**: Phones, cameras, and cars already had the silicon and tooling to support them.
## 3\. Business Applications: Where Value Shows Up Today
The impact of Edge AI is already visible in consumer products. Here’s where users are benefiting:
- **Smartphones**: Assistants, dictation, translation, and camera guidance now work offline. Speed, privacy, and reduced cloud cost are the payoff.
- **Home devices**: Voice and gesture recognition feels instant and private when kept local. Engagement improves as a result.
- **Wearables and health**: Always-on sensing and safety features such as fall detection deliver timely insights without needing connectivity.
Beyond consumers, industries and the public sector are also realizing gains. Use cases include:
- **Automotive**: Perception and monitoring run on-vehicle for guaranteed latency; in-cabin assistants ensure offline control.
- **Smart cameras and retail**: On-camera analytics cut uplink bandwidth by 10–100×, avoiding costly upgrades.
- **Manufacturing**: Local quality inspection improves consistency and reduces material waste.
- **Healthcare**: On-device imaging accelerates diagnosis while protecting patient privacy.
- **Smart infrastructure**: Traffic systems running at the edge reduce congestion and CO₂ emissions.
For executives, the value comes down to familiar levers. Edge AI delivers in five main ways:
- **Latency and engagement**: Faster responses (up to 200 ms sooner) feel better and drive usage.
- **Bandwidth and TCO**: Processing at the edge reduces data transmission and cloud egress costs.
- **Privacy/compliance**: Keeping more data local supports regulatory obligations.
- **Offline resilience**: Devices continue to function during outages and backfill when reconnected.
- **Sustainability**: Less backhaul traffic and server load translates into lower energy use.
## 4\. Future Implications: The Next 3–5 Years
So, what comes next? Based on research and market signals, here’s what looks most likely:
- **On-device first**: Phones, cars, cameras, and wearables will increasingly handle speech, vision, and assistant tasks locally.
- **Efficiency above all**: Compression, pruning, quantization, and distillation will drive competitiveness.
- **Hybrid by design**: Routine tasks remain local; complex queries move to privacy-preserving cloud.
- **Platform consolidation**: Standardized runtimes will make developers’ lives easier.
- **Continuous foresight**: Patent and research monitoring will become a key strategic tool.
Still, several open questions remain. Leaders should watch for these uncertainties:
- **Capability vs. thermals**: Can devices handle richer models without heat or battery trade-offs?
- **Fragmentation**: Will developer tooling unify performance across ecosystems?
- **Energy accounting**: Metrics like “joules per request” are still missing but will shape procurement.
- **Regulation**: Safety, medical, and privacy rules will dictate what must stay local.
- **Global coverage**: International signals will broaden the view beyond U.S. patents.
Finally, executives should start by asking themselves a few pointed questions:
- **User journeys**: Which experiences in your product would be better if instant, private, and offline?
- **Cost structure**: Where do your expenses scale with usage, and could Edge AI reduce them?
- **Device readiness**: What hardware and runtimes do your customers’ devices support today?
- **Fleet management**: How will you validate, sign, and safely update models across thousands of devices?
- **Signals to track**: Which patents, papers, or launches will you monitor quarterly—and who owns the process?
## 5\. References
#### 5.1 Core Definitions and Standards
- [NIST SP 500-325 — Fog Computing Conceptual Model (Final)](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.500-325.pdf)
- [ETSI GS MEC 001 V2.1.1 — Multi-access Edge Computing; Terminology](https://www.etsi.org/deliver/etsi_gs/MEC/001_099/001/02.01.01_60/gs_mec001v020101p.pdf)
- [ISO/IEC TR 23188:2020 — Edge Computing Landscape](https://www.iso.org/standard/75288.html)
- [LF Edge — Open Glossary of Edge Computing](https://wiki.lfedge.org/display/GLOSS/Open+Glossary+of+Edge+Computing)
- [Google — What is on-device processing?](https://developers.google.com/machine-learning/mobile/ml-kit/on-device)
- [Apple — Core ML Overview](https://developer.apple.com/documentation/coreml)
- [tinyML Foundation — About](https://www.tinyml.org/about/)
#### 5.2 Technical Enablers
- [Han et al. (2015), _Deep Compression_](https://arxiv.org/abs/1510.00149)
- [Krishnamoorthi (2018), _Quantization for Efficient Inference_](https://arxiv.org/abs/1806.08342)
- [Hinton et al. (2015), _Knowledge Distillation_](https://arxiv.org/abs/1503.02531)
- [MobileNetV2 (2018)](https://arxiv.org/abs/1801.04381)
- [MobileNetV3 (2019)](https://arxiv.org/abs/1905.02244)
- [EfficientNet (2019)](https://arxiv.org/abs/1905.11946)
- [TVM (2018)](https://arxiv.org/abs/1802.04799)
- [TensorFlow Lite (2017)](https://developers.googleblog.com/en/announcing-tensorflow-lite/)
- [ONNX Runtime Mobile (2020)](https://onnxruntime.ai/inference)
- [NVIDIA TensorRT](https://docs.nvidia.com/tensorrt/)
#### 5.3 Evidence of Mainstream Adoption
- [Google I/O 2019: Next-gen Assistant (on-device)](https://blog.google/products/assistant/next-generation-google-assistant-io/)
- [Pixel Recorder — On-device Transcription](https://research.google/blog/the-on-device-machine-learning-behind-recorder/)
- [iOS 15 — On-device Siri](https://www.apple.com/newsroom/2021/09/ios-15-is-available-today/)
- [Samsung Galaxy AI — Live Translate](https://www.samsungmobilepress.com/press-releases/enter-the-new-era-of-mobile-ai-with-samsung-galaxy-s24-series/)
- [Google AICore / Gemini Nano — On-device Runtime](https://ai.google.dev/gemini-api/docs/get-started/android_aicore)
#### 5.4 Enterprise, Industrial, and Public Sector
- [EU General Safety Regulation — Driver Monitoring](https://single-market-economy.ec.europa.eu/news/mandatory-drivers-assistance-systems-expected-help-save-over-25000-lives-2038-2024-07-05_en)
- [Mobileye EyeQ Shipments](https://ir.mobileye.com/news-releases/news-release-details/mobileye-releases-fourth-quarter-and-full-year-2024-results-and/)
- [Axis Zipstream — Bandwidth Reduction](https://whitepapers.axis.com/en-us/axis-zipstream-technology)
- [Cisco Meraki MobileOne Case Study](https://meraki.cisco.com/customers/mobileone-llc/)
- [FDA: Caption Guidance — AI-guided Ultrasound](https://www.fda.gov/news-events/press-announcements/fda-authorizes-marketing-first-cardiac-ultrasound-software-uses-artificial-intelligence-guide-user)
- [Philips SmartSpeed Precise — MRI Acceleration](https://www.usa.philips.com/a-w/about/news/archive/standard/news/articles/2025/philips-advances-mri-speed-and-precision-with-fda-510k-clearance-of-smartspeed-precise-dual-ai-software)
- [NVIDIA Jetson/Metropolis — Traffic Optimization](https://developer.nvidia.com/blog/metropolis-spotlight-marshallai-optimizes-traffic-management-while-reducing-carbon-emissions)
### What Is GEO? A Guide to Generative Engine Optimization for Businesses
URL: https://www.mycustomai.io/blog/what-is-geo-a-guide-to-generative-engine-optimization-for-businesses
Summary: Search is shifting from “ten blue links” to AI-generated answers powered by citations and shortlists. This blog introduces Generative Engine Optimization (GEO)—the new discipline of ensuring your brand is cited, trusted, and included in AI responses. We cover how engines select sources, why earned media matters, where GEO creates value across the customer journey, and what new KPIs leaders should track.
## Introduction — What is Generative Engine Optimization, and Why It Matters Now
Imagine asking _“best Wi-Fi for a 3-bedroom home”_ and getting one concise answer, a short shortlist, and just a few citations. Your customer may never see a page of links—or your homepage.
In this moment, visibility is no longer about “ranking on page one.” It’s about being chosen as evidence inside the AI’s answer.
**Generative Engine Optimization (GEO)** is the discipline of increasing a brand or source’s inclusion and prominence inside AI-generated answers. In practical terms, GEO means aligning your information with the sources generative engines retrieve and trust, and structuring it so machines can quickly parse, verify, and attribute it.
Success is measured by:
- **Citation presence and share** (are you cited, and how often?),
- **Prominence inside the answer** (earlier or primary citations carry more weight), and
- **Inclusion in algorithmic recommendation sets** (AI-curated shortlists).
That’s very different from SEO’s rank-and-CTR game.
## Key Terms, Simply Put
- **Generative engine**: An AI that searches the web (or connected data), writes an answer, and shows provenance (citations or verification controls). Examples: ChatGPT Search, Perplexity, Claude with Web Search, Microsoft Copilot, Google AI Overviews.
- **Earned media**: Third-party, authoritative coverage (e.g., expert reviews, trusted publications). Engines rely most on these.
- **Brand content**: Your own site and properties.
- **Social/community**: User-generated sources (e.g., Reddit, YouTube, forums). Treatment varies by engine and topic.
- **Shortlist**: A small, AI-curated set of recommended options presented in the answer.
- **Citation share**: Your share of the sources an engine cites across a set of queries.
## Background — From “Ten Blue Links” to an Answer-First, Citation Economy
- **How engines answer today.** Modern AI search retrieves from multiple sources, synthesizes a coherent response, and exposes provenance through inline citations, source panels, or verification controls \[OpenAI; Perplexity; Anthropic; Microsoft; Google\].
- **Why GEO ≠ SEO.** Classic SEO optimized for ranking. GEO optimizes for being one of a few sources chosen inside an AI answer or shortlist \[KDD’24 GEO\].
- **The behavioral shift.** When AI summaries appear, users click traditional links less often—and rarely click the citations—making _“presence in the answer”_ economically meaningful \[Pew\].
- **Engines differ materially.** Some lean heavily on earned media; others are fresher and more diverse (including retailers or video). Overlap between AI citations and organic top-10 results is partial—even within the same provider \[Search Engine Land\].
- **Language and locality matter.** Engines often change sources when the query language changes. Local outlets carry weight even when global authorities exist \[Chen et al., 2025\].
- **Fast facts from research.** Chen et al. (2025) highlight:
- Strong bias toward earned media,
- Low domain overlap across engines,
- Language choice > phrasing for source selection,
- Big-brand bias on unbranded prompts,
- Distinct engine “personalities.”
- **Open risks and debates.** Citation accuracy is inconsistent. Engines are improving grounding and transparency, but attribution and manipulation risks remain \[Tow Center; Anthropic\].
## Business Applications — Where GEO Creates Value
_(Note: Engines behave differently by category, market, and language. Use these as lenses to assess opportunity and risk.)_
#### 1\. Discovery and Shortlist Inclusion
- **Inclusion inside answers.** Many sessions end at the AI block. Visibility depends on citation or shortlist presence—especially in categories like comparisons and troubleshooting \[Chen et al., 2025\].
- **Local services.** Proximity matters, but authoritative local coverage and structured details (services, hours, insurance) drive inclusion \[Whitespark\].
_Example:_ A regional clinic with structured service pages and credible local press coverage is more likely to be cited than one with sparse details.
#### 2\. Agentic Shopping and Conversion
- **From answers to actions.** Engines increasingly compare, check prices, and even enable purchasing. Structured, fresh product facts are essential \[Google; Microsoft\].
- **Early signals.** Travel and retail pilots show conversion lifts when assistants lead the flow \[Trip.com; Klarna\].
_Example:_ A mid-market appliance brand with clearly marked specs, pricing, and return policies is more likely to be recommended.
#### 3\. Post-Purchase Service and Deflection
- **Support content AIs can cite.** Concise, current support articles with clear steps and policy summaries help assistants resolve queries \[Klarna\].
_Example:_ Publishing a “Returns at a glance” section improves the odds your policy is cited accurately.
#### 4\. B2B Buying and Advisor Workflows
- **Vendor shortlists.** Expert reviews and structured comparisons shape which vendors appear in AI-built lists.
- **Internal copilots.** GEO-aligned content boosts answer quality inside enterprises too.
#### 5\. Measurement and Analytics
- **New KPIs.** Track AI-answer coverage rate, citation share, top-citation placement, shortlist presence, and freshness lag.
- **Tying to outcomes.** Compare pre/post AI answer trends; focus on directional signals, not exact counts.
#### 6\. Ecosystem and Partnerships
- **The citation economy.** Brands need authoritative reviews and guides—plus strong first-party structure. Publisher partnerships and licensing can also help \[Perplexity; OpenAI\].
- **Social/community dynamics.** Engines treat Reddit, YouTube, and forums differently; monitor and diversify.
### 7\. Policy and Governance
- **Transparency and competition.** Regulators are scrutinizing AI search, big-brand bias, and publisher economics. Expect higher demands for provenance standards (e.g., C2PA) and potential compensation models \[EU AI Act; FTC\].
## Future Implications — What’s Next?
#### Where the Trendline Points
- **Shortlists = shelf space.** A handful of citations become the discovery layer.
- **Earned authority as gatekeeper.** Expert outlets wield influence.
- **Agentic commerce by default.** Structured, updated data is essential.
- **Global means local.** Winning requires localized credibility.
#### Key Challenges
- **Measurement standards.** No agreed-upon KPIs yet.
- **Reliability and trust.** Citation accuracy remains patchy.
- **Bias and equity.** Big-brand and language biases disadvantage smaller players.
- **Manipulation resistance.** Agents introduce new risks.
- **Publisher economics.** Concentrated exposure pressures revenue.
#### Open Questions
- Will engines adopt signed, per-answer “content receipts”?
- Can niche brands beat big-brand bias with deep, verifiable expertise?
- How will GEO operations scale globally?
- What KPI set will boards accept for GEO performance?
## References
**Core Research and Definitions**
1\. Aggarwal, K., et al. _Generative Engine Optimization (GEO)._ KDD 2024. [https://arxiv.org/abs/2311.09735](https://arxiv.org/abs/2311.09735)
2\. Chen, M., Wang, X., Chen, K., Koudas, N. _Generative Engine Optimization: How to Dominate AI Search._ (2025). [http://arxiv.org/abs/2509.08919v1](http://arxiv.org/abs/2509.08919v1)
**Provider Documentation and Product Behavior**
3\. Google: _AI Overviews and your website._ [https://developers.google.com/search/docs/appearance/ai-overviews](https://developers.google.com/search/docs/appearance/ai-overviews)
4\. OpenAI: _Introducing ChatGPT Search._ [https://openai.com/index/introducing-chatgpt-search/](https://openai.com/index/introducing-chatgpt-search/)
5\. OpenAI Help: _ChatGPT Search._ [https://help.openai.com/en/articles/9237897-chatgpt-search](https://help.openai.com/en/articles/9237897-chatgpt-search)
6\. Perplexity: _What is Pro Search?_ [https://www.perplexity.ai/help-center/en/articles/10352903-what-is-pro-search](https://www.perplexity.ai/help-center/en/articles/10352903-what-is-pro-search)
7\. Anthropic: _Citations API._ [https://www.anthropic.com/news/introducing-citations-api](https://www.anthropic.com/news/introducing-citations-api)
8\. Microsoft: _Copilot Transparency Note._ [https://support.microsoft.com/en-us/topic/transparency-note-for-microsoft-copilot-c1541cad-8bb4-410a-954c-07225892dbc2](https://support.microsoft.com/en-us/topic/transparency-note-for-microsoft-copilot-c1541cad-8bb4-410a-954c-07225892dbc2)
**Behavioral Impact and Audits**
9\. Pew Research: _Users click less when AI summaries appear._ [https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/](https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/)
10\. Search Engine Land: _AI Overviews citations from deep pages._ [https://searchengineland.com/google-ai-overviews-citations-deep-pages-453414](https://searchengineland.com/google-ai-overviews-citations-deep-pages-453414)
11\. Nieman Lab: _AI citations fail >60% accuracy tests._ [https://www.niemanlab.org/2025/03/ai-search-engines-fail-to-produce-accurate-citations-in-over-60-of-tests-according-to-new-tow-center-study/](https://www.niemanlab.org/2025/03/ai-search-engines-fail-to-produce-accurate-citations-in-over-60-of-tests-according-to-new-tow-center-study/)
**Localization and Language**
12\. Google: _AI Overviews expansion to 200+ countries and 40+ languages._ [https://blog.google/products/search/ai-overview-expansion-may-2025-update/](https://blog.google/products/search/ai-overview-expansion-may-2025-update/)
**Local and Operational Studies**
13\. Whitespark: _AI Overviews in Local Search._ [https://whitespark.ca/blog/case-study-the-prevalence-of-ai-overviews-in-local-search/](https://whitespark.ca/blog/case-study-the-prevalence-of-ai-overviews-in-local-search/)
14\. Local Falcon: _Impact on Local Businesses._ [https://www.globenewswire.com/news-release/2025/05/21/3085651/0/en/Local-Falcon-Study-Uncovers-How-Google-AI-Overviews-Are-Impacting-Local-Businesses.html](https://www.globenewswire.com/news-release/2025/05/21/3085651/0/en/Local-Falcon-Study-Uncovers-How-Google-AI-Overviews-Are-Impacting-Local-Businesses.html)
**Agentic Shopping and Service**
15\. Trip.com newsroom: _TripGenie conversion lift._ [https://www.trip.com/newsroom/tripgenie-new-features-2/](https://www.trip.com/newsroom/tripgenie-new-features-2/)
16\. Klarna: _AI assistant performance._ [https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/)
**Ecosystem and Partnerships**
17\. Perplexity: _Publishers Program._ [https://www.theverge.com/2024/7/30/24208979/perplexity-publishers-program-ad-revenue-sharing-ai-time-fortune-der-spiegel](https://www.theverge.com/2024/7/30/24208979/perplexity-publishers-program-ad-revenue-sharing-ai-time-fortune-der-spiegel)
18\. OpenAI–publisher partnerships. [https://www.ft.com/content/33328743-ba3b-470f-a2e3-f41c3a366613](https://www.ft.com/content/33328743-ba3b-470f-a2e3-f41c3a366613)
**Provenance and Standards**
19\. C2PA Technical Specification. [https://c2pa.org/specifications/specifications/2.1/specs/C2PA\_Specification.html](https://c2pa.org/specifications/specifications/2.1/specs/C2PA_Specification.html)
### The Agentic Retail Revolution: Redefining E-Commerce in the GenAI Era
URL: https://www.mycustomai.io/blog/the-agentic-retail-revolution-redefining-e-commerce-in-the-genai-era
Summary: Your Business vs. The AI Agent Economy AI agents aren't just recommending products—they're buying them. Amazon agents shop competitors' sites. Shopify lets any AI buy from millions of stores. The shift from human-driven to agent-driven commerce is happening now. The Critical Question: Who controls the agent that shops for your customers? Marketplace agents optimize for platform profits. LLM agents optimize for user satisfaction. The same request gets completely different recommendations depending on the agent type. New Revenue Streams: Agent-specific pricing, influence fees, and sponsored placement create entirely new ways to monetize. Your Choice: Will you shape how agents recommend your products, or be subject to agents controlled by competitors? Companies that act now get first-mover advantages in the agent economy.
## **The Current State: From Browsing to Buying with AI**
We're witnessing the early stages of a fundamental shift in how people shop. What started as simple AI Agents answering customer service questions has evolved into sophisticated AI agents capable of making purchasing decisions on behalf of consumers. The evidence is mounting across major platforms:
**Amazon's "Buy for Me" Agent** represents perhaps the most aggressive move into agentic commerce. Amazon is testing an AI agent that shops third-party sites when Amazon doesn't sell something, powered by Amazon's Nova AI models and Anthropic's Claude. This isn't just a recommendation—it's active procurement.
**Shopify's Agentic Commerce Platform** uses the Model Context Protocol (MCP) to let AI agents like ChatGPT and Claude interact directly with merchant storefronts—searching products, managing carts, and handling checkout across their entire ecosystem. This MCP infrastructure democratizes AI shopping, allowing small boutiques to offer the same agent-powered experience as major retailers.
## **Why Agents Will Reshape the Funnel**
The traditional marketing funnel—Intent → Awareness → Conversion → Payment → Loyalty—assumes human decision-making at every stage. AI agents are changing this equation dramatically, but not uniformly across all stages.
### Agent Influence Across the Funnel
| Stage | Agent Influence | Stage Conversion |
| --- | --- | --- |
| Intent & Need Recognition | High | 100% of users |
| Awareness & Discovery | High | 85% continue |
| Conversion | Moderate | 65% convert |
| Purchase | Friction zone | 45% complete |
| Loyalty | High | 80% retention |
### High Influence Zones: Intent and Awareness
AI agents excel at the top of the funnel, where their influence is strongest and most valuable to businesses. Unlike traditional marketing that waits for consumers to express intent, agents can proactively predict and even create needs:
- **Predictive Intent Generation:** "Based on your calendar, you have a work trip next week. Should I book your usual hotel and order travel-sized toiletries?"
- **Cross-Category Discovery:** "Your running shoes are due for replacement based on your weekly mileage. I found three options that match your gait pattern."
- **Latent Need Recognition:** "Your grocery patterns suggest you're cooking more Asian cuisine lately. These specialty ingredients are on sale this week."
This represents a fundamental shift from reactive commerce (responding to expressed needs) to predictive commerce (anticipating unstated needs).
### The Conversion Sweet Spot
At the conversion stage, agents provide moderate but sophisticated influence. They excel at optimization tasks—finding the best specifications, comparing alternatives, and analyzing value propositions. However, this is where the question of who designs the agent becomes critical.
### Trust: The Missing Layer
Here's where the agent revolution hits its biggest obstacle. Payment represents the irreversible decision point—the moment where recommendation becomes financial commitment. The Salesforce data reveals the critical trust factors: shoppers rank data privacy and security protections, the ability to easily turn agents on/off, and requiring approval before any purchase as their top requirements for trusting AI agents.
The friction isn't just psychological; it's practical. The generational divide is stark: Gen Z shoppers are 2.7x more likely than baby boomers to want product recommendations from AI agents (63% vs 23%), yet even among younger users, trust mechanisms remain essential.
Users develop personalized trust thresholds based on historical accuracy of agent recommendations. A user might fully automate $20 grocery purchases but require validation for anything over $100, or any purchase in an unfamiliar category—similar to how developers review AI-generated code before deployment.
Post-purchase, agents regain high influence through automated reordering, predictive restocking, and loyalty optimization. This is particularly powerful for habitual purchases where decision fatigue is high and brand switching costs are low.
## **Who's Actually Making the Decisions?**
### The Agentic E-Commerce Ecosystem
| Agent Type | Primary Incentives | Revenue Models |
| --- | --- | --- |
| **Marketplace Agents** | Maximize platform GMV; push higher-margin products; optimize inventory turnover; keep users within ecosystem | Transaction fees; advertising revenue; sponsored recommendations; data monetization |
| **LLM Provider Agents** | Optimize user satisfaction; increase agent engagement; drive model usage; build user trust | API usage fees; subscription models; partnership commissions; premium features |
| **Third-Party Agents** | Specialized use cases; values-based shopping; niche optimization; speed & efficiency | Commission-based; SaaS subscriptions; consulting services; data insights |
| **Hybrid / Custom Agents** | Enterprise customization; workflow integration; data privacy control; business-logic alignment | Enterprise licensing; custom development; integration services; support contracts |
The most critical question in agentic commerce isn't technological—it's economic. **Who owns and operates your shopping agent determines what you buy.** The current landscape reveals four distinct types of agents, each with fundamentally different incentives:
**Marketplace Agents** (like Amazon's "Buy for Me") optimize for platform metrics. Their primary goals are maximizing gross merchandise volume (GMV), pushing higher-margin products, optimizing inventory turnover, and keeping users within their ecosystem. When Amazon's agent recommends a product, it's likely considering not just your needs, but also Amazon's business objectives—inventory levels, profit margins, and competitive positioning.
**LLM Provider Agents** (like ChatGPT shopping or Claude) optimize for user satisfaction and continued engagement. Their revenue comes from API usage, subscriptions, and maintaining user trust rather than individual transactions. This creates different incentives—they're more likely to recommend the objectively best product for your needs, even if it's not the most profitable for any single retailer.
**Third-Party Specialized Agents** focus on niche optimization and values-based shopping. A sustainability-focused agent might only recommend products meeting specific environmental criteria, while a price-optimization agent might always choose the cheapest option. These agents typically operate on commission models or subscription fees, creating yet another set of incentives.
**Hybrid/Custom Enterprise Agents** serve business customers with customized logic, workflow integration, and data privacy controls. These agents align with specific organizational procurement policies and approval workflows.
The implications are profound: the same shopping request could yield entirely different recommendations depending on which type of agent you're using. A marketplace agent might suggest a higher-margin alternative, while an LLM provider agent might recommend the most popular option, and a specialized agent might prioritize sustainability or price above all else.
## **The Agent Economy: New Rules, New Revenue**
### New Monetization Opportunities
**For Retailers**
- Agent-specific pricing
- Agent influence fees
- API access premiums
- Sponsored agent placement
**For Platforms**
- Agent orchestration fees
- Cross-platform transaction fees
- Agent performance data
- Trust & verification services
This shift to agentic commerce creates entirely new revenue streams and business models that didn't exist in traditional e-commerce:
**For Retailers**, the opportunities include agent-specific pricing (offering different prices to agents versus human shoppers), agent influence fees (paying to get prioritized in agent recommendations), API access premiums (charging for agent-friendly product data feeds), and sponsored agent placement (ensuring visibility in agent search results). Smart retailers are already experimenting with "agent-first" product information—structured data that helps agents make better recommendations while highlighting key selling points.
**For Platforms**, new revenue streams emerge from agent orchestration fees (charging for facilitating multi-platform transactions), cross-platform transaction fees, selling agent performance analytics, and providing trust and verification services for agent-mediated purchases. The companies that build the infrastructure connecting agents to commerce will likely capture significant value.
**For Agent Providers**, monetization extends beyond simple transaction commissions to include premium agent personalities (pay extra for an agent trained on specific influencer preferences), specialized industry knowledge (agents optimized for restaurant procurement or medical supplies), and white-label agent solutions for retailers wanting to offer branded shopping assistants.
Perhaps most significantly, we're seeing the emergence of **agent influence fees**—retailers paying to ensure their products are recommended by popular agents, similar to how Google Ads works today but applied to conversational commerce.
## **How Agentic Commerce is Actually Built**
The current landscape reveals three distinct approaches to agentic commerce, each with different technical architectures and business models:
1. **Direct Integration Protocols** (Anthropic's MCP): Conversational agents access merchant APIs directly
2. **Agent-to-Agent Frameworks** (Google's A2A protocol): Systems where agents communicate with other agents to complete transactions
3. **Hybrid Web-Browsing Approaches**: Agents that can navigate traditional e-commerce websites as well as use specialized APIs
Each approach creates different monetization opportunities for retailers. MCP requires merchants to build agent-friendly APIs. Agent-to-agent frameworks create new intermediary roles. Web-browsing agents can work with existing infrastructure but may miss agent-specific optimization opportunities.
## **How to Win in Agentic Commerce**
We're not just witnessing the automation of shopping—we're seeing the emergence of a new **influence economy** where the power to shape purchasing decisions is shifting from traditional marketing channels to AI systems.
The companies that understand this shift earliest—and design their strategies around agent-mediated commerce—will capture disproportionate value in the coming decade. Those that don't risk being disintermediated by more agent-friendly competitors.
The question isn't whether agents will reshape retail—it's whether your business will shape how agents recommend products to consumers, or whether you'll be subject to the recommendations of agents controlled by your competitors.
_In our next section, we'll explore the trust and control frameworks that will determine which products consumers are willing to delegate to agents, and which require human oversight._
## **Ready to Navigate the Agent Revolution?**
The transformation to agentic commerce is accelerating faster than most businesses realize. The companies that act now—understanding both the market dynamics and technical implementation—will have significant first-mover advantages.
**For Business Leaders**: Book a **discovery call** with our team to assess how agentic commerce will impact your industry and develop a strategic roadmap for the next 18 months.
**For Teams & Organizations**: Join our upcoming **"Agentic Commerce Masterclass"** where we'll dive deep into implementation strategies, competitive positioning, and ROI frameworks for AI shopping agents.
**For Enterprise Teams**: Schedule a **custom workshop** for your leadership team. We'll analyze your specific market position, competitive threats, and opportunities in the agent economy—from both market design and AI implementation perspectives.
[Schedule a Discovery Call](/schedule-discovery-call)
## **References**
1. Amazon "Buy for Me" AI Shopping Agent Testing. [_TechCrunch_](https://techcrunch.com/2025/04/03/amazons-new-ai-agent-will-shop-third-party-stores-for-you/), April 2025.
2. Shopify Agentic Commerce Platform Launch. [_Shopify Developer Documentation_](https://shopify.dev/docs/agents), 2024.
3. Shopify Storefront MCP Server Integration. [_Shopify Developer Documentation_](https://shopify.dev/docs/apps/build/storefront-mcp), 2024.
4. "How MCP-UI Powers Shopify's New Commerce Widgets in Agents." [_The New Stack_](https://thenewstack.io/how-mcp-ui-powers-shopifys-new-commerce-widgets-in-agents/), August 2025.
5. "The race is on to make AI agents do your online shopping for you." [_TechCrunch_](https://techcrunch.com/2024/12/02/the-race-is-on-to-make-ai-agents-do-your-online-shopping-for-you/), December 2024.
6. "Salesforce reveals AI agent retail trends for 2025." [_Salesforce_](https://www.salesforce.com/news/stories/ai-agent-retail-trends-2025/), March 2025.
7. "Connected Shoppers Report, 6th Edition." [_Salesforce_](https://www.salesforce.com/content/dam/web/en_us/www/documents/research/connected-shoppers-report-6th-edition.pdf), 2024.
8. Anthropic Model Context Protocol (MCP) [Documentation](https://docs.anthropic.com/en/docs/mcp). _Anthropic_, 2024.
9. "Announcing the Agent2Agent Protocol (A2A)." [_Google Developers Blog_](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/), April 2025.
10. What is Your AI Agent Buying? Evaluation, Implications, and Emerging Questions for Agentic e-Commerce. Agentic e-Commerce Research. [ACE blog](https://ace.mycustomai.io/), 2025
### Cost-Efficient AI Infrastructure Strategies for Enterprises
URL: https://www.mycustomai.io/blog/cost-efficient-ai-infrastructure-strategies-for-enterprises
Summary: This blog explores how enterprises can reduce AI infrastructure costs while maintaining quality and compliance. It highlights strategies such as reusing existing hardware, keeping compute close to data to avoid egress fees, and coordinating smaller models to achieve large-model performance at lower cost. Practical takeaways show how organizations can start with edge or regulated workloads, right-size models, and measure costs “at quality” for sustainable AI adoption.
## Introduction
Cost-efficient AI infrastructure strategies are about one thing: **getting the most value per dollar while meeting strict quality, speed, and compliance requirements.**
Instead of just counting GPU hours, this approach measures **total cost of ownership per successful task**—where “successful” means the task meets service-level objectives (SLOs) for accuracy, latency, availability, and compliance.
Why this matters now:
- AI workloads run across cloud, on-premises, and edge. Costs come from more than GPUs—data egress, energy, software licensing, observability, and compliance all play a role.
- Benchmarks like MLPerf and HELM measure efficiency “at quality,” not just raw accuracy.
- Enterprises must now optimize cost **per SLO-compliant task** while managing latency, energy, and resilience.
## Background: How We Got Here
Three forces reshaped the economics of AI infrastructure:
1. **Hardware Economics**
- Idle PCs and workstations with consumer GPUs can undercut cloud GPU rentals for small/medium models.
- Data egress costs are rising, making data locality critical.
- Energy/facility overheads vary—on-prem can beat hyperscale if workloads are steady and local.
- Software advances (quantization, batching, vLLM optimizations) made smaller hardware far more powerful.
2. **Distributed Systems Foundations**
- Concepts like gossip protocols, local discovery, and decentralized orchestration matured.
- Early projects (Petals, Hivemind) showed coordination without central controllers can work.
3. **Modeling Research**
- Techniques like self-consistency, cascaded routing, and mixture-of-agents (MoA) proved **small coordinated models can rival larger single models.**
- A 2025 example, **Symphony**, coordinated 7B-class models across consumer GPUs, outperforming centralized baselines with minimal overhead.
## Business Applications
1. **Regulated On-Prem Workloads**
- Use cases: healthcare PHI processing, imaging triage, branch KYC checks.
- Benefits: lower egress, simplified compliance, deterministic latency.
2. **Edge and Field Operations**
- Use cases: in-store analytics, warehouse robotics, predictive maintenance.
- Benefits: millisecond control loops, offline resilience, reduced egress.
3. **Enterprise Knowledge Work**
- Use cases: private RAG, BI text-to-SQL, code maintenance, meeting summarization.
- Benefits: reuses existing hardware, avoids per-token API costs, matches larger model quality at lower cost.
4. **Multi-Party Collaboration Without Data Sharing**
- Use cases: interbank fraud detection, cross-hospital triage, supplier quality networks.
- Benefits: diverse data insights without moving data, lower legal friction, federated compliance.
## Measuring Cost-Efficiency
- Define “good” tasks with clear SLOs (accuracy, latency, compliance).
- Report **cost per good task**—including compute, storage, energy, egress, licensing, and compliance.
- Always disclose:
- Energy per task
- Latency distribution (p95/p99)
- Carbon footprint
- Coordination overheads (e.g., Symphony adds <5% latency).
## Future Outlook
1. **Hybrid, Edge-First Fabric** – modest local devices + central clusters for heavy tasks.
2. **Small Model Democratization** – coordinated ensembles close the gap with big models.
3. **Standards for Trust & Interoperability** – portable capability schemas, audit records, attestation.
4. **Locality = Efficiency** – lower latency, lower energy by keeping compute near data.
5. **Offline Resilience** – decentralized systems degrade gracefully during outages.
**Open questions:** scaling economics, security against fake nodes, interoperability standards, precision trade-offs, resilience under churn.
## Practical Takeaways
- Start where **locality is mandatory** (healthcare, finance, manufacturing).
- Pick **decomposable workflows** (support triage, report generation, QA).
- **Reuse existing hardware** and right-size models.
- **Measure cost at quality**, not just raw throughput.
- Build **lightweight orchestration** and **audit trails**.
- Plan for **governance, resilience, and churn.**
## References
- [Google SRE on SLOs](https://cloud.google.com/blog/products/devops-sre/sre-fundamentals-slis-slas-and-slos/)
- [FinOps AI Cost Estimation](https://www.finops.org/framework/capabilities/unit-economics/)
- [MLPerf Inference Benchmarks](https://mlcommons.org/2024/03/mlperf-inference-v4/)
- [HELM Benchmark](https://ar5iv.labs.arxiv.org/html/2211.09110)
- [Symphony Framework (2025)](https://arxiv.org/abs/2508.20019v1)
### Compliance‑Aware AI Systems
URL: https://www.mycustomai.io/blog/compliance-aware-ai-systems
Summary: How compliance-aware AI differs from responsible AI: systems with built-in audit logs, provenance tracking, and human-in-the-loop oversight for AML investigations, KYC, sanctions screening, and regulated industries.
## Introduction
What does it mean for AI to be “compliance-aware”?
In plain English, it’s an AI system that:
- Builds rules and safeguards directly into how it works.
- Keeps a record of everything it does (documentation, logs, validation checks).
- Adapts as laws, risks, and data evolve.
Think of it like a **flight recorder + checklist + co-pilot**: the AI helps you get the job done, records every step, and ensures a human can always review and decide.
How this differs from “responsible AI”:
- _Responsible AI_ gives you broad principles (fairness, transparency).
- _Compliance-aware AI_ goes further. It provides **proof**—auditable evidence that the system meets regulatory obligations.
## Background
Why is this approach emerging now?
- **Regulators set the bar.** From anti-money laundering (AML) laws to the EU AI Act, regulators demand not just accuracy but **documentation, oversight, and audit trails**.
- **AI Agents fell short.** Standard large language models can sound fluent, but they often “make things up,” lack provenance, and leave gaps in privacy and logging—unacceptable in regulated environments.
- **A new design pattern took shape.** Institutions began converging on best practices:
- Linking every claim to its source.
- Designing privacy into data intake.
- Using independent “AI-as-judge” quality checks.
- Keeping humans in control of decisions.
- Preserving immutable logs for full accountability.
_Quick glossary:_
- **SAR:** A regulatory suspicious activity report.
- **Typology:** A pattern of financial crime (e.g., romance scam).
- **Agentic AI:** Multiple AI agents that plan, check, and balance each other, see [this blog](/blog/what-is-ai-agent-and-why-is-it-important) for foundationals.
- **Provenance:** The “who/what/when/where” of data, prompts, and outputs.
## Business Applications
Compliance-aware AI isn’t theoretical. Here are some high-impact use cases:
1. **AML/SAR Investigations**
- **What it does:** Collects evidence, identifies suspicious patterns, drafts a defensible narrative, and runs automated checks before handing off to an investigator.
- **Why it helps:** Saves time and improves consistency in a process where deadlines are tight and errors are costly.
- **Real results:** In pilot testing, “Co-Investigator AI” cut drafting time by **61%**, improved narrative completeness by **70%+**, and still kept human reviewers firmly in control.
2. **Enhanced Due Diligence (EDD) & KYC**
- Automates identity resolution, checks ownership, and summarizes adverse media with credibility tags—leaving a clear audit trail.
3. **Sanctions Screening**
- Explains why a match was flagged, applies escalation rules, and produces regulator-ready documentation—reducing false positives and speeding up decisions.
4. **Credit Risk Reviews**
- Drafts exception reports with citations and sends them for independent review—shortening cycles while preserving accountability.
5. **Insurance & Claims Fraud**
- Builds case chronologies, summarizes findings, and flags red signals—reducing case handling time and improving completeness.
6. **Legal, E-Discovery, and Audit**
- Produces defensible summaries, redacts sensitive data, and generates audit workpapers tied directly to evidence.
**What leadership notice most:**
- Faster throughput, fewer backlogs.
- Higher consistency and fewer quality defects.
- Stronger audit readiness with end-to-end logs.
- Reduced cost and risk exposure.
## Future Implications (2025–2028)
Where is compliance-aware AI headed?
- **Policy & Standards:** The EU AI Act will make logging, documentation, and human oversight _mandatory_ for high-risk AI. U.S. banking regulators will continue enforcing strict model and third-party risk rules.
- **Architectures:** One-shot AI Agents will give way to modular, human-in-the-loop systems with privacy guards, audit logs, and independent validation layers.
- **Data & Evaluation:** Gold-standard datasets and provenance tracking will become the foundation for reliable performance measurement.
- **Operating Models:** AI co-pilots will sit inside case management systems, with KPIs tracking timeliness, completeness, and audit readiness.
**Open questions for leaders to watch:**
- How much automation is acceptable before regulators push back?
- What does “meaningful” human oversight look like in practice?
- How should firms reconcile logging with strict confidentiality rules?
- Will global standards emerge for audit logs and provenance?
## References
For readers who want to go deeper, key sources include:
- [NIST AI Risk Management Framework](https://doi.org/10.6028/NIST.AI.100-1)
- [EU Artificial Intelligence Act](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689)
- [ISO/IEC 42001: AI Management Systems](https://www.nqa.com/en-us/certification/standards/iso-42001)
- [Federal Reserve SR 11-7 – Model Risk Management](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.pdf)
- [Co-Investigator AI research (2025)](http://arxiv.org/abs/2509.08380v1)
### From Demo to Deployment: The Reliability Gap in AI Agents
URL: https://www.mycustomai.io/blog/from-demo-to-deployment-the-reliability-gap-in-ai-agents
Summary: Autonomous task completion reliability—the ability of AI agents to consistently finish real-world, multi-step tasks—is now the core standard for readiness. While demos showcase potential, enterprises must evaluate process integrity, repeatability, and observability to unlock safe, scalable business value.
## Introduction: What “autonomous task completion reliability” means
In simple terms, this is the likelihood that an AI agent can complete an end-to-end, multi-step workflow correctly in a live setting—using the right tools, with the right inputs, within real-world time and cost limits.
Why it matters now:
- Many systems that shine in demos falter in deployment, often solving fewer than 60% of realistic tasks.
- Failures usually stem from flawed processes—wrong tool, wrong input, missed steps—rather than incorrect answers.
- Recent benchmarks show that even frontier models struggle to deliver consistent reliability on complex, tool-using tasks.
Quick TL;DR for enterprises:
- Don’t assume autonomy translates from demos to production.
- Reliability = success + sound, auditable process.
- Guardrails and observability matter as much as model choice.
## Background: From great demos to process-aware reliability
AI has shifted from producing answers to executing actions. Early reasoning improvements helped on static problems, but business value depends on reliable execution across systems and tools.
Standardized tool protocols made integration easier but expanded the decision space—and with it, the opportunity for errors. Evaluations in live settings consistently reveal that performance plateaus without better planning, tool use, and validation.
The key lesson: scaling models is not enough. Process awareness and reliability engineering determine whether autonomy is truly enterprise-ready.
## Business Applications: Where reliability pays off
- **E-commerce returns**: correct refunds and shipping depend on reliable ID checks and policy validation.
- **Travel disruptions**: consistent enforcement of refund rules and timelines prevents costly compliance issues.
- **Telecom plan changes**: reliable autonomy ensures proper verification and authorization.
- **Banking and insurance**: agents succeed when escalation paths and auditability are engineered in.
- **Order-to-cash**: automation value comes from repeatability—high match rates and predictable outcomes.
Practical metrics to track: automated resolution rate, tool-call accuracy, stability across re-runs, efficiency (cost/time per success), and full auditability.
## Future Implications: What improves reliability, what remains hard
Near-term levers to improve reliability:
- **Plan-first strategies**: break down tasks before execution.
- **Schema-aware generation**: reduce format mistakes by aligning with tool definitions.
- **Validators and repair loops**: detect and correct bad inputs before submission.
- **Curated tool routing**: fewer, more relevant tools improve outcomes.
- **Observability and process metrics**: transparent traces make failures diagnosable and fixable.
Big questions ahead:
- How can we balance planning vs. compute budgets?
- Will schema constraints and validators be enough to reduce wrong-value errors in production?
- How do we judge long, complex workflows fairly and at scale?
- Can agents remain robust as content, data, and tools evolve?
For enterprises, the implication is clear: reliability will increasingly define procurement standards, vendor comparisons, and deployment strategies—not just accuracy scores or demo appeal.
## References
##### Core definitions & governance
- **NIST AI RMF Resource Center** – Characteristics of valid, reliable AI and human oversight.
[https://airc.nist.gov/airmf-resources/airmf/3-sec-characteristics/](https://airc.nist.gov/airmf-resources/airmf/3-sec-characteristics/)
- **EU AI Act** – Logging and post-market monitoring requirements.
[https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689)
- **ISO/IEC 42001 (AI management systems)** and **ISO/IEC 27001** (logging/monitoring).
https://www.iso.org/standard/42001
##### Reliability benchmarks & methods
- **LiveMCP-101** – Stress testing agents on multi-tool tasks.
[http://arxiv.org/abs/2508.15760](http://arxiv.org/abs/2508.15760)
- **WebArena** – Realistic web environment for autonomous agents.
[https://arxiv.org/abs/2307.13854](https://arxiv.org/abs/2307.13854)
- **OSWorld** – Benchmarking multimodal agents in real computer environments.
[https://arxiv.org/abs/2404.07972](https://arxiv.org/abs/2404.07972)
- **ReAct: Synergizing Reasoning and Acting** – Connecting reasoning to tool use.
[https://arxiv.org/abs/2210.03629](https://arxiv.org/abs/2210.03629)
- **Toolformer** – Language models learning to use tools.
[https://arxiv.org/abs/2302.04761](https://arxiv.org/abs/2302.04761)
##### Business & enterprise case studies
- **Klarna AI assistant** – Handling two-thirds of customer service chats.
[https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/)
- **Lufthansa AI service agents** – Scaling disruption handling.
[https://www.cognigy.com/en/case-study/lufthansa](https://www.cognigy.com/en/case-study/lufthansa)
- **Bank of America Erica** – Billions of automated interactions with reliability at scale.
[https://newsroom.bankofamerica.com/content/newsroom/press-releases/2024/04/bofa-s-erica-surpasses-2-billion-interactions--helping-42-millio.html](https://newsroom.bankofamerica.com/content/newsroom/press-releases/2024/04/bofa-s-erica-surpasses-2-billion-interactions--helping-42-millio.html)
- **Order-to-cash automation benchmarks** – High auto-match rates in cash application.
[https://www.thehackettgroup.com/ai-software-e-payments-hackett-cash-vendors/](https://www.thehackettgroup.com/ai-software-e-payments-hackett-cash-vendors/)
### Biggest Strengths and Limitations of LLMs
URL: https://www.mycustomai.io/blog/llms-top-strengths-and-worst-weaknesses
Summary: Discover the transformative power and limitations of Large Language Models (LLMs) like ChatGPT in our latest blog. Explore how they excel in tasks like text summarization and creative writing, yet face challenges in areas like factual accuracy and complex reasoning. Delve into a comprehensive analysis of their biggest strengths, limitations, and the future of AI technology, offering insights for users and developers alike.
Text Summarization
Initial drafts for writing a blog/essay
Code Generation
Hallucinations
Reasoning
Easy
Hard
_Use Cases: from easy to hard_
## Introduction
Large Language Models (LLMs) such as ChatGPT have become indispensable to Artificial Intelligence (AI) technology, providing unparalleled capabilities across many industries. However, it's essential to acknowledge that these models come with certain limitations, and to harness their full potential while mitigating the risks; one must thoroughly understand their biggest strengths and limitations.
## Biggest strengths of LLMs
#### Text Summarization Capabilities
Through extensive training on massive text datasets, large Language Models (LLMs) like ChatGPT have honed the ability to distill long and complex texts into concise, coherent summaries. This proficiency is invaluable for professionals needing to quickly grasp the essence of lengthy reports, research papers, or articles. LLMs go beyond mere truncation, identifying and extracting key points to ensure the summary encapsulates the core message of the original text.
Additionally, LLMs can contextually prioritize information, discerning what is most relevant to the topic. This ability stems from their training in diverse literary and informational texts.
For instance, an LLM can effectively highlight key findings and methodology in summarizing a medical research paper, focusing on the most critical elements without being sidetracked by less pertinent details.
This feature is particularly valuable in fields like law or academia, where sifting through extensive documents is routine.
#### Generation of Initial Drafts for Creative Writing
In creative writing, LLMs demonstrate exceptional skill in generating initial drafts for various output forms, such as blogs and narratives. Their versatility is rooted in their exposure to a wide range of textual inputs during training, which includes everything from classic literature to modern web content.
This exposure allows them to adapt to various narrative tones and styles, making them versatile tools for creative tasks. The models serve as a creative scaffold, aiding the ideation process and enhancing human creativity. They can mimic specific writing styles, providing a customizable base for writers to develop further and refine.
For example, a blogger can utilize an LLM to generate a draft on a specific topic and then infuse personal insights and edits, creating a final piece that resonates with their unique voice.
#### Assistance in Writing and Refining Programming Code
For software development, LLMs have become indispensable tools. They aid in generating code snippets, suggesting improvements, and debugging. Their utility stems from training on vast code repositories, equipping them to understand common programming patterns and practices.
LLMs can interpret the intent behind a coding query and suggest contextually appropriate code segments, considering different programming languages and frameworks. This proficiency extends to understanding various programming languages and frameworks, thanks to the diverse coding datasets included in their training.
For instance, a developer working on a complex algorithm can use an LLM to generate a code structure or suggest optimization strategies, significantly speeding up the development process. This makes them valuable for developers across various platforms and languages, providing insights that lead to more efficient and optimized code.
## Limitations of LLMs
#### Hallucination
A significant limitation of LLMs is their tendency to generate plausible but inaccurate or misleading information, known as "hallucination." This phenomenon arises from their training methodology, which focuses on the likelihood of word sequences rather than factual accuracy.
LLMs prioritize the generation of text that appears factually sound based solely on patterns learned from their training data, without any means to verify current or factual correctness. For instance, an LLM might generate a financial report with convincing but entirely fabricated figures.
This limitation becomes especially problematic in rapidly evolving fields like news, technology, or science, where outdated or incorrect information can lead to significant misunderstandings or detrimental decisions if used without verification. ([Wikipedia: Hallucination in AI](https://en.wikipedia.org/wiki/Hallucination_\(artificial_intelligence\))).
#### Limitations in Reasoning Capabilities
LLMs exhibit limitations in tasks requiring complex reasoning, particularly in mathematics. Their auto-regressive design, adept at creating fluent language output, needs to improve in logical reasoning and complex problem-solving. In solving mathematical problems, such as "6 ÷ 2(1+2)", where the correct answer is 1, an LLM might incorrectly compute it as 9. This error results from the model's focus on pattern recognition over logical mathematical principles.
The model's training does not inherently include mathematical rules or the order of operations, leading to errors in calculation. These limitations are more evident in complex and larger numerical problems, underscoring a fundamental architectural limitation in handling tasks that require sequential logical processing or real-time data interpretation.
We invite you to check the following [document](https://arxiv.org/pdf/2309.03241.pdf) for more details about LLMs' capabilities in dealing with complex reasoning.
#### Security Vulnerabilities
LLMs are vulnerable to a variety of security threats, including jailbreaks, prompt injections, and data poisoning. These attacks often exploit the model's language prediction capabilities to extract or insert harmful content. For example, jailbreaks and prompt injections involve manipulating the model's responses through carefully crafted inputs, such as disguising a harmful request as an innocent query. This can trick the model into providing unsafe or unethical information.
On the other hand, data poisoning introduces corrupted training data, embedding hidden triggers that activate under certain conditions, leading to compromised or unintended behaviour. This type of attack can cause the model to behave in a manner akin to a "sleeper agent," activated by specific phrases or contexts.
The presence of biased outputs, where the models replicate and amplify biases in their training data, is also a significant concern. This is particularly critical in applications requiring neutrality and fairness, such as legal or HR scenarios. The potential for bias and vulnerabilities to manipulation and data poisoning underscores the need for continuous monitoring and ethical guidelines in deploying LLMs in sensitive areas.
## Conclusion
Based on the insights that have been presented, it is evident that Language Model AI systems, such as ChatGPT, have the potential to be transformative in many ways. However, it is important to acknowledge that their current implementation may be associated with inherent risks and limitations.
As technology evolves, LLM issues will eventually be addressed. To ensure that LLMs are reliable and secure, continuously improving their design, training, and security protocols is important. However, until these advancements are made, users must remain critical of LLM outputs and use them only as supplements to human judgment and expertise.
## Ready to Overcome the Limitations of LLMs?
While powerful, LLMs like ChatGPT carry inherent risks—such as hallucinations, limited reasoning capabilities, and security vulnerabilities. At **My Custom AI**, we specialize in turning these limitations into strengths:
- **Feasibility Studies** to pinpoint the right AI solutions for your use case.
- **Custom AI Development & Deployment** tailored for accuracy, reliability, and security.
- **AI Professional Training** to empower your team in managing AI risks effectively.
[**Contact us today**](/schedule-discovery-call) and harness AI’s full potential for your business.
## References
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2023). Language Models are Few-Shot Learners. Retrieved from [arXiv:2309.03241](https://arxiv.org/pdf/2309.03241.pdf).
- Wikipedia contributors. (n.d.). Hallucination (artificial intelligence). In Wikipedia, The Free Encyclopedia. Retrieved from [https://en.wikipedia.org/wiki/Hallucination\_(artificial\_intelligence)](https://en.wikipedia.org/wiki/Hallucination_\(artificial_intelligence\)).
- XDA-Developers. (n.d.). Why Large Language Models like GPT-3 are wrong at math. Retrieved from [https://www.xda-developers.com/why-llms-are-bad-at-math/](https://www.xda-developers.com/why-llms-are-bad-at-math/).
### AI Agent: What Is It, and Why Is It Important?
URL: https://www.mycustomai.io/blog/what-is-ai-agent-and-why-is-it-important
Summary: Discover how AI Agents—autonomous systems powered by LLMs, memory, tools, and actions—are reshaping industries by enabling dynamic, collaborative, and high-performance automation across complex tasks.
## Introduction
AI Agents represent the future of artificial intelligence. Jensen Huang, CEO of Nvidia, [has emphasized this by stating](https://finance.yahoo.com/news/nvidia-jensen-huang-says-ai-044815659.html) that AI agents present "a multi-trillion-dollar opportunity" and asserting, "the age of AI Agents is here." In this blog, we will clearly explain what AI Agents are and why understanding their significance is crucial, especially for businesses looking to thrive in the rapidly evolving tech landscape.
## What is AI Agent?
#### Defining an Agent
The industry has not yet settled on a universally agreed-upon definition of "agent." Some companies refer to these entities as "operators," yet the most fitting description highlights an autonomous system at its core. Here’s how we break it down:
- **Autonomous System:** Capable of independent operation.
- **LLM as OS/Orchestrator:** Systems like [MemGPT](https://arxiv.org/pdf/2310.08560v1) and [AIOS](https://arxiv.org/pdf/2403.16971?) utilize Large Language Models (LLMs) as operating systems or orchestrators to manage operations.
- **Enhanced Capabilities:** Agents are equipped with:
- **Memory:** To recall and utilize past information.
- **Tools:** To interact effectively with external systems and data.
- **Actions:** To carry out specific tasks autonomously.
#### What Makes AI "Agentic" / AI Agents pipeline / Crew?
AI Agents refer to systems composed of multiple specialized agents organized into pipelines, each handling distinct tasks but collaboratively working towards overarching goals. Think of it like a highly specialized team, where each member is an expert focused on their individual role, yet all members synchronize seamlessly to achieve complex objectives.
## Why are AI Agents Important?
#### Dynamic Adaptability
AI Agents systems offer unparalleled flexibility and efficiency through:
- **Dynamic Logic:** Unlike traditional software, there’s no need for rigid, pre-defined logic because these agents dynamically adapt their logic by leveraging various tools.
- **Dynamic Memory:** Instead of overwhelming systems by storing vast amounts of information, agents dynamically access relevant data from memory as needed.
- **Adaptive Actions:** Based on logic and retrieved memory, agents decide autonomously which actions to execute, enhancing responsiveness and performance.
#### Industry Transformation
AI Agents will fundamentally alter industries by embedding intelligent automation across all digital workflows. For example:
- **Coding Agents:** Solutions like [Cursor](https://techcrunch.com/2024/12/19/in-just-4-months-ai-coding-assistant-cursor-raised-another-100m-at-a-2-5b-valuation-led-by-thrive-sources-say/) and [WindSurf](https://windsurf.com/editor) significantly enhance software engineering productivity.
- **Research Agents:** Automating complex information gathering and analysis, see [Deep Research Agent](https://openai.com/index/introducing-deep-research/) by Open AI.
- **Computer Use Agents:** Automating everyday computer tasks, significantly improving productivity, see [Agent S](https://www.simular.ai/articles/agent-s2).
Such advancements particularly impact white-collar professions, notably roles closely aligned with technology, like software engineering and tasks traditionally executed by junior-level employees. Dario Amodei, CEO of Anthropic, [notes that AI may eliminate up to half of entry-level white-collar roles](https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic).
## The Future of AI Agents
#### Strategic Perspective
With foundational AI models approaching market saturation, as [envisioned](https://youtu.be/WQQdd6qGxNs?si=fxeqTa00EPO7tg6Q&t=476) by Ilya Sutskever and confirmed by benchmark plateauing, future value generation will shift predominantly to the business application layer, precisely where AI Agents thrive by leveraging these foundational models more effectively. Even the best AI foundational models are trying to make acquisition in order to move to the application layer, see [OpenAI attempt to acquire Windsurf](https://www.reuters.com/business/openai-agrees-buy-windsurf-about-3-billion-bloomberg-news-reports-2025-05-06/).
#### Technical Evolution
The next phase for AI Agents involves embodied agents, integrating AI with physical systems—a concept initially explored by OpenAI and now vigorously pursued by emerging firms like [Physical Intelligence (π)](https://www.physicalintelligence.company/) and established leaders such as [Nvidia’s Generalist Embodied Agent Research Lab](https://research.nvidia.com/labs/gear/).
## Conclusion
AI Agents represent a transformative shift toward intelligent, autonomous systems capable of dynamic problem-solving and adaptation. Future blogs will delve into specific agent protocols like MCP and A2A, along with recommendations on frameworks for developing your own agents. Understanding and harnessing AI Agents is not just beneficial—it's imperative for staying competitive in the evolving digital era.
### What is Retrieval-Augmented Generation (RAG)?
URL: https://www.mycustomai.io/blog/what-is-retrieval-augmented-generation-rag
Summary: Explore how Retrieval-Augmented Generation (RAG) revolutionizes Large Language Models (LLMs) by enhancing information precision and relevance through extensive data retrieval, ensuring efficient information management and productivity gains.
## **1\. Introduction**
Retrieval-augmented generation (RAG) is a transformative approach in natural language processing that enables Large Language Models (LLMs) to provide more precise and relevant information by retrieving data from extensive knowledge sources.
Implementing this technology overcomes traditional LLMs' prompt length constraints. It ensures that even the most comprehensive queries are handled effectively, thus enabling more efficient information management.
This technological breakthrough represents a significant advancement in the field, offering a solution to a long-standing problem that has hindered the progress of many projects. By capitalizing on this technology, businesses and organizations can optimize their workflows and improve their productivity while enhancing the quality of their outputs.
### **2\. The Imperative for RAG**
The need for Retrieval-Augmented Generation (RAG) emerges primarily from two reasons. The first is the prompt capacity limitations in LLMs, crucial for analyzing large-scale datasets like those found in genomic research. Such datasets require parsing vast amounts of data beyond what a standard prompt can handle. RAG overcomes this by selectively sourcing relevant data, thus facilitating more informed responses from the LLM.
Secondly, RAG ensures that LLMs remain current and informative by incorporating the latest information from external sources. This continuous update mechanism is essential for maintaining the relevance and accuracy of LLM-generated content across various domains.
### **3\. How does Retrieval-Augmented Generation (RAG) Work?**
[Conceptual Overview of RAG in LLMs](https://aws.amazon.com/what-is/retrieval-augmented-generation/)
Retrieval-augmented generation operates through a sophisticated, multi-step process to enhance the information processing capabilities of LLMs:
- **Initiating the Query:** It begins with a user's input, which forms the initial prompt. This prompt may contain a specific question or a topic that requires further information.
- **Retrieving Information:** Upon receiving the query, the RAG system searches various knowledge sources to find relevant information.
- **Enhancing Context for LLMs:** The retrieved information is then synthesized to form an enhanced context.
- **Response Generation:** Armed with this context, the LLM can now generate a response informed by the most relevant and current information.
- **Feedback Loop for Contextual Relevance:** The final step involves using the generated response to refine the context for subsequent queries.
For a detailed exploration of retrieval-augmented generation for knowledge-intensive NLP tasks, refer to the comprehensive paper available [here](https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf).
### **4\. Real-world Applications of RAG**
RAG's utility is evident in its ability to digest extensive knowledge bases, such as the multitude of pages on Amazon's website. When an LLM like GPT encounters a query related to such an extensive database, it employs RAG to extract and utilize only the necessary information, avoiding the impracticality of processing an excessive volume of data.
#### **Example: Healthcare Information Synthesis**
In the healthcare industry, RAG can quickly compile the latest research findings to assist medical professionals in diagnosing and treating rare diseases. By providing the most current medical insights, RAG can save lives.
#### **Example: RAG for legal**
Law firms can use RAG to sift through extensive legal databases to find relevant case law, helping lawyers to craft more informed legal strategies and arguments based on the latest precedents.
#### **Example: Financial Market Analysis**
Financial analysts can employ RAG to pull the latest market reports and data trends, ensuring their investment advice reflects the most recent market conditions.
### **5\. The Evolution of LLMs with RAG**
Incorporating RAG into LLMs represents a significant advance in NLP. This integration extends beyond mere data retrieval; it allows for dynamic updating of an LLM’s knowledge base. Consequently, LLMs can access and incorporate recent information and developments in various fields, ensuring accurate responses reflect the latest understanding and discoveries. This adaptability is essential in sectors where new data, such as medical research or technology, emerges rapidly.
Using RAG, LLMs can maintain relevance over time without needing labor-intensive retraining. This evolution signifies a move towards more agile, informed, and context-aware artificial intelligence systems.
#### **5.1 RAG vs. Semantic Search**
While RAG involves retrieving relevant documents to enhance the LLM's context, semantic search is about understanding the query's intent and the contextual meaning of the terms. RAG uses semantic search to pinpoint the most relevant information for the LLM to process. This nuanced interplay allows RAG to go beyond mere keyword matching, engaging in a deeper analysis of the query's underlying meaning. This integration enables LLMs to respond more precisely, ensuring the information provided is contextually relevant and semantically aligned with the user's intent. For more details, refer to the [paper](https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf).
#### **5.2 Google Search vs Perplexity**
Google Search and Perplexity illustrate distinct approaches to information retrieval and processing. Google Search, utilizing semantic search, focuses on interpreting the intent behind user queries to deliver relevant results. In contrast, Perplexity, integrating RAG capabilities, enhances responses by accessing various external knowledge sources, aiming for accuracy and context relevance. This demonstrates the contrast between traditional search methodologies and the advanced, context-aware processing enabled by RAG technologies.
### **6\. Conclusion**
Implementing RAG is a stepping stone towards more autonomous, intelligent systems. Soon, RAG could revolutionize how we interact with digital assistants, making them indispensable tools for various professional and personal tasks. As we stand on the brink of this technological leap, it is clear that RAG will be a key driver in the next wave of AI applications.
### **7\. References**
1. Lewis, P., Perez, E., et al. (2020). _Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks._ In NeurIPS 2020. Retrieved from [NeurIPS Proceedings](https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf).
2. Amazon Web Services. (n.d.). _What Is RAG?_ Retrieved from [AWS](https://aws.amazon.com/what-is/retrieval-augmented-generation/).
3. Gao, Y., Xiong, Y., et al. (2023). _Retrieval-Augmented Generation for Large Language Models: A Survey_. Retrieved from [arXiv](https://arxiv.org/pdf/2312.10997v1.pdf).
4. Stanford University. (2023). CS25: V3 I _Retrieval Augmented Language Models._ Retrieved from [YouTube](https://www.youtube.com/watch?v=mE7IDf2SmJg&t=468s).
### Navigating the AI Landscape: A Comparative Guide to Open Source vs Commercial AI Models
URL: https://www.mycustomai.io/blog/ai-landscape-open-source-vs-commercial-ai-guide
Summary: A detailed comparison between open source and commercial AI models, focusing on their definitions, popular examples, and key selection criteria. Open source AI models, exemplified by Llama 2, BLIP, and Whisper, are highlighted for their community-driven development, flexibility, and scalability. Commercial AI models like ChatGPT and DALL·E 3 are noted for their proprietary nature, focused research, and specialized services. A significant part of the discussion is dedicated to choosing between the models based on scale and task specificity, illustrated with a Gantt chart. The post concludes by emphasizing that while these criteria are crucial, other factors like cost, maintenance, and support are also vital in the decision-making process, promising further exploration in upcoming posts.
### Open Source AI Models
#### Defintion
Open source AI models are advanced machine learning frameworks that are accessible under licenses allowing their use and distribution, including for commercial applications. These models are typically the result of a collective effort from a community of developers and are made available on platforms like [Hugging Face](https://huggingface.co/), a central repository for collaborative AI model development. Although the exact training datasets are often not transparent, the open source nature of these models enables users to explore, modify, and build upon the model architecture and pre-trained weights to tailor them for a variety of applications.
#### Popular examples
- **Llama 2 by Meta:**The Llama 2 suite from Meta showcases models designed for effective human-like text generation and dialogue, demonstrating competitive capabilities in conversational AI without compromising safety and utility.****
- **BLIP: Vision-Language Pre-training:** BLIP advances unified vision-language tasks by innovatively filtering web-derived data to enhance both comprehension and generative tasks, achieving leading results across several benchmarks.****
- **Whisper:** The Whisper by OpenAI model is recognized for its robust automatic speech recognition, trained on extensive labeled datasets, delivering adaptable performance across various languages and contexts.
For more details comparison about the latest open source LLMs, we invite to check the following [leaderboards](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). There are other leaderboards for vision and audio models as well.
### Commercial AI Models
#### Defintion
Commercial AI models are proprietary offerings from private entities, featuring advanced support and specialized services. These closed-source models require purchase or subscription for access and are a result of focused research. They are tailored for specific applications, providing performance advantages and customer support to enterprises.
#### Popular examples
- **ChatGPT** by OpenAI is a leading commercial conversational AI that customizes dialogue through prompt engineering. Initially free, it now follows a freemium model, with advanced features available via subscription.****
- **DALL·E 3** is another OpenAI innovation, a text-to-image model that creates digital art from textual prompts, showcasing the commercial application of AI in creative fields.
For comparison between open source models and commercial model, we invite the reader to check the following [leaderboard](https://chat.lmsys.org/?leaderboard).
### **Open Source vs Commercial AI: Scale and Task Specificity**
In the decision-making process for selecting AI models, many factors come into play. Here, we concentrate on two primary considerations: scale and task specificity, which often guide the choice between open source and commercial options.
- **Scale** Open source AI models are particularly advantageous when the application demands high scalability. They are adept at parallelizing thousands of requests, a necessity for large-scale operations. This is in contrast to commercial AI models, which typically impose rate limits, potentially throttling the number of API calls per minute and affecting response times during high-demand periods.
****
- **Task Specificity** For tasks requiring specialized knowledge or expertise, open source AI models are often preferable. They can be precisely tailored and trained on specialized datasets, a flexibility not always available with commercial AI models. These commercial models are designed for broad applicability but might not cater to niche requirements with the same level of specificity.
While numerous other factors will influence the final choice, scale and task specificity stand out as the main ones:
> For scalability and intensive parallel processing, open source AI is the strategic choice. For general use without the need for domain-specific tuning, commercial AI models may suffice.
Choosing between Open Source and Commercial AI
Task Specificity
Specific Task
General Task
Open Source AI
Commercial AI
Small Scale
High Scale
Scale
_Figure: Strategic Comparison of Open Source vs. Commercial AI Models_
The chart above is a strategic guide to choosing between open source and commercial AI models based on two fundamental criteria: Scale and Task Specificity. The x-axis represents the scale, ranging from small to high, indicating the volume of requests or operations the model can handle. The y-axis represents task specificity, ranging from general tasks to highly specific ones, signifying the model's ability to handle specialized requirements.
Commercial AI models (shown in blue) typically excel at small-scale, general tasks due to their broad applicability and inherent rate limits. Open source AI models (depicted in green), with their flexibility and scalability, are more suited to high-scale, specific tasks, as they allow for extensive customization and parallel processing capabilities. This visualization aids in making an informed decision based on the operational needs and specific requirements of a project or application.
In conclusion, while scale and task specificity are critical factors in choosing between open source and commercial AI models, other essential considerations such as cost, maintenance, support, and customization also play a vital role in the decision-making process. Stay tuned for upcoming posts where we will delve deeper into these aspects to provide a comprehensive perspective on selecting the most suitable AI model for your needs.
### AI training data annotation tools
URL: https://www.mycustomai.io/blog/ai-training-data-annotation-tool
Summary: Learn about the crucial role of training data annotation in AI systems, and discover the unique features of Label Studio, Labelbox, AWS Sagemaker GroundTruth, and Scale AI.
## **Introduction:**
In the realm of artificial intelligence (AI), training data annotation is essential for enabling machine learning algorithms to interpret data effectively. The quality of annotations directly influences AI system performance, making the choice of annotation tool crucial. This article compares four leading annotation platforms: Label Studio, Labelbox, AWS Sagemaker GroundTruth, and Scale AI, highlighting their features, integrations, and suitability for various annotation needs. Understanding the importance of training data annotation and selecting the right tool is vital for building accurate and robust AI models.
### Data Approach Comparison
| Feature | Label Studio | Labelbox | AWS Sagemaker GroundTruth | Scale AI |
| --- | --- | --- | --- | --- |
| Pricing | Freemium | Subscription-based | Pay-as-you-go based on usage | Custom pricing based on project |
| Open vs Closed source | Open-source | Closed-source | Closed-source | Closed-source |
| Modality | Images, Text, Audio, Video | Images, Text, Video | Images, Text, Audio, Video | Images, Text, Audio, Video |
| Integration with Database Providers | Integration with AWS S3, Google Cloud Storage | Export/import data | Direct integration with AWS storage | Export/import data |
| Workflow review | Manual review | Collaboration tools | Built-in workflows | Dedicated project management |
| Setup | Requires setup on a server | Cloud-based | Managed service | Managed service |
### **1\. What is training data annotation?**
Training data annotation involves the process of labeling or tagging raw data to make it understandable for machines. This labeling provides context and meaning to data, enabling machine learning algorithms to learn from examples and make accurate predictions. Annotation can take various forms depending on the type of data and the machine learning task at hand, including categorization, bounding box annotation, segmentation, transcription, and more.
#### **1.1. Impact of Quality Annotations on AI Performance**
The quality of training data annotations plays a crucial role in the effectiveness of [AI systems](/what-is-custom-ai), directly impacting how well they perform and make accurate predictions. When annotations are done well, machine learning models can learn important information from the data, allowing them to understand complex patterns and trends accurately. This helps create AI solutions that are dependable and can handle various situations effectively. However, if annotations are not done properly, they can cause serious problems by introducing biases, errors, and mistakes into the learning process. These issues can confuse the model and lead to incorrect predictions in real-life situations. That's why it's vital to carefully create training data annotations, ensuring that AI systems can understand their environments and operate with accuracy.
#### **1.2. Different Types of Training Data Annotation**
Annotations can take various forms depending on the task and the type of data:
- **Classification:** This annotation type involves assigning labels or categories to data points based on their characteristics. For instance, in image classification tasks, each image is labeled with a specific class, such as "cat" or "dog," indicating the presence of particular objects or concepts within the image. Classification is fundamental in various machine learning tasks, including sentiment analysis, text categorization, and image recognition.
- **Bounding Box Annotation:** Bounding box annotation entails drawing rectangular boxes around objects or regions of interest within an image to precisely indicate their location and extent. Each bounding box typically represents a single object instance, facilitating object detection, localization, and tracking tasks. This annotation type is crucial for applications like autonomous vehicles, where identifying and localizing objects in real-time is essential for safe navigation.
- **Segmentation:** Segmentation involves delineating the outline or boundary of objects within an image, often pixel by pixel, to separate them from the background. Unlike bounding boxes, segmentation provides more detailed and precise object delineation, enabling pixel-level understanding of image content. Semantic segmentation assigns a semantic label to each pixel, categorizing them into different object classes or regions. This level of granularity is particularly useful in medical imaging, where accurate delineation of organs or abnormalities is critical for diagnosis and treatment planning.
- **Transcription:** Transcription is the process of converting non-textual data, such as audio recordings or handwritten documents, into text format. In audio transcription, spoken words are transcribed into written text, enabling the analysis and processing of spoken language data. This annotation type is essential for applications like speech recognition, virtual assistants, and language translation, where understanding and processing spoken language are paramount.
- **Semantic Annotation:** Semantic annotation involves adding metadata or tags to data to describe its content or context. This metadata provides additional information about the data, facilitating search, retrieval, and understanding. For example, in text annotation, news articles may be annotated with topics, keywords, or sentiment scores to enable content-based search and analysis. Semantic annotation is prevalent in information retrieval, content management, and knowledge organization systems.
### **2\. A Look at Popular Training Data Annotation Platforms:**
#### **2.1. Label Studio:**
##### **2.1.1. Description:**
[Label Studio](https://labelstud.io/) is an open-source data labeling tool developed by Heartex. It offers a versatile platform for data annotation tasks, supporting various data types like text, images, video, and audio. With Label Studio, users can create custom labeling interfaces, collaborate with team members, and integrate machine learning models for active learning workflows. Its flexibility and extensibility make it a popular choice for machine learning projects requiring labeled data.
##### **2.1.2. Characteristics:**
- **Pricing:** Free and open-source.
- **Integration with Database Providers:** Supports exporting annotated data to various formats and has integrations with major cloud providers.
- **Workflow Review:** Provides flexible workflow management and review features.
- **Setup:** Requires setup on a server, but comprehensive documentation is available to facilitate installation and configuration.
##### **2.1.3. Example:**
****An analytics company could utilize a platform like Label Studio to annotate user-generated images for sentiment analysis. Customizing annotation workflows ensures accurate labeling of emotions depicted in images, facilitating the analysis of user sentiment towards products or brands based on social media image posts.
#### **2.2. Labelbox:**
##### **2.2.1. Description:**
[Labelbox](https://labelbox.com/product/annotate/) is a comprehensive data labeling platform designed to streamline the process of creating high-quality labeled datasets for machine learning applications. It offers a user-friendly interface for annotating various types of data, including images, text, and video. Labelbox provides tools for data management, collaboration, quality control, and integration with machine learning pipelines. Its scalability and customization options make it suitable for both small-scale projects and large-scale enterprise deployments.
##### **2.2.2. Characteristics:**
- **Pricing:** Subscription-based with different tiers based on usage.
- **Integration with Database Providers:** Supports integration with cloud storage providers like AWS S3 and Google Cloud Storage.
- **Workflow Review:** Advanced workflow management and review features.
- **Setup:** Cloud-based, easy to set up and manage with no server setup required.
##### **2.2.3. Example:**
Dialpad, a company specializing in AI-driven customer engagement solutions, faced challenges with data quality in their AI projects. They turned to Labelbox for higher-quality training data and reduced labeling costs. For instance, in transcribing customer calls accurately, they utilized Labelbox to streamline the labeling process. Labelers listened to audio clips, transcribed sentences, and noted any issues like background noise. This approach ensured high-quality data for training their transcription model, ultimately improving accuracy.
#### **2.3. AWS Sagemaker GroundTruth:**
##### **2.3.1. Description:**
[AWS Sagemaker GroundTruth](https://aws.amazon.com/sagemaker/groundtruth/) is a managed data labeling service provided by Amazon Web Services (AWS). It simplifies the process of labeling large datasets for training machine learning models. With SageMaker Ground Truth, users can access a workforce of human labelers or utilize automated labeling techniques to annotate data accurately and efficiently. The service integrates seamlessly with other AWS services, such as Amazon SageMaker, for end-to-end machine learning workflows.
##### **2.3.2. Characteristics:**
- **Pricing:** Pay-as-you-go pricing model based on usage.
- **Integration with Database Providers:** Direct integration with AWS services for data storage.
- **Workflow Review:** Provides basic workflow review capabilities.
- **Setup:** Managed service, no setup required, accessible through the AWS Management Console.
##### **2.3.3. Example:**
****The NFL employs AWS SageMaker Ground Truth to meticulously annotate football game images, ensuring precise detection of helmets in varying scenarios. By leveraging this annotated dataset, they train their helmet detection models using state-of-the-art algorithms within Amazon SageMaker. By utilizing SageMaker Ground Truth, they sought to automate the detection of helmet impacts in football game footage, a task traditionally requiring manual review. Their goal was to develop models capable of identifying helmet-to-helmet, helmet-to-shoulder, and other collisions, ultimately enhancing player safety protocols and informing game strategies.
#### **2.4. Scale AI:**
##### **2.4.1. Description:**
[Scale AI](https://scale.com/data-engine) is a data labeling and training data platform that offers a combination of human and machine intelligence to create high-quality labeled datasets for artificial intelligence applications. It provides a scalable workforce of human labelers and advanced machine learning algorithms to annotate various types of data, including images, video, and LiDAR. Scale AI's platform offers tools for data management, quality control, and integration with machine learning pipelines, catering to the needs of businesses across different industries.
##### **2.4.2. Characteristics:**
- **Pricing:** Custom pricing based on project requirements and volume.
- **Integration with Database Providers:** No direct integration, but supports exporting annotated data to various formats.
- **Workflow Review:** Provides dedicated project management and quality assurance.
- **Setup:** Managed service, no setup required, accessible through the Scale AI platform.
##### **2.4.3. Example:**
****Optimus Ride, a Boston-based company specializing in autonomous vehicle development for geo-fenced environments, faced challenges in labeling their growing dataset in-house. The expansion into new environments necessitated a partner for more efficient and accurate data labeling. They chose Scale AI for its ability to provide labeled data quickly and at a higher quality than internal efforts. The partnership allows Optimus Ride to adapt to customer needs, scale deployments, and ensure the practical use and enduring value of their technology.
### **3\. How to choose the right tool**
Annotation tools play a crucial role in machine learning and AI development by facilitating the labeling and annotation of data. Selecting the appropriate annotation tool is essential to ensure accurate and efficient data labeling for training machine learning models. Here's a guide to help you navigate through different annotation tools and choose the one that best suits your requirements.
- **Label Studio:**
**When to choose:** Opt for Label Studio if you need a versatile annotation tool supporting multiple data modalities such as images, text, audio, and video. It's ideal for those who prefer open-source solutions and possess the technical expertise to customize the tool. Label Studio offers flexibility in annotation types and workflows, along with integration capabilities with various machine learning frameworks for model training.
- **Labelbox:**
**When to choose:** Choose Labelbox for a comprehensive annotation platform equipped with advanced features and support for various annotation types including images, text, and video. It's suitable for users seeking a cloud-based solution that's easy to set up and manage, with built-in collaboration tools and workflow management capabilities. Labelbox also provides seamless integration with cloud storage providers like AWS S3 or Google Cloud Storage for efficient data management.
- **AWS Sagemaker GroundTruth:**
**When to choose:** Opt for AWS Sagemaker GroundTruth if you're already using AWS services and prefer a fully managed data labeling solution integrated within the AWS ecosystem. It offers scalability and automation for annotation tasks, along with built-in workflows supporting both human and machine labeling. GroundTruth also provides direct integration with AWS storage services for seamless data transfer and management within the AWS environment.
- **Scale AI:
****When to choose:** Consider Scale AI if you require high-quality annotations for computer vision tasks such as image and LiDAR annotation. It offers dedicated project management and quality assurance, making it suitable for users who prefer a managed service approach with expert assistance and fast turnaround times for annotation projects. Scale AI is particularly beneficial for those with specific or complex annotation tasks requiring customization and personalized support.
### **Conclusion:**
In conclusion, the selection of the right training data annotation tool is paramount for ensuring the accuracy and efficiency of AI systems. Each platform—Label Studio, Labelbox, AWS Sagemaker GroundTruth, and Scale AI—offers unique features and capabilities tailored to diverse annotation needs. Whether it's the versatility of Label Studio, the comprehensive functionality of Labelbox, the seamless integration with AWS services provided by Sagemaker GroundTruth, or the high-quality annotations and dedicated project management offered by Scale AI, understanding the requirements of your project is essential for making the optimal choice. By leveraging the insights provided in this comparison, developers and data scientists can make informed decisions to build robust AI models capable of addressing real-world challenges effectively.
### Need Help Selecting or Implementing the Right Annotation Tool?
Choosing the ideal annotation platform significantly impacts the quality of your AI models. At **My Custom AI**, we help businesses like yours:
- **Evaluate and choose** the most effective annotation solution tailored specifically to your data and AI goals.
- **Integrate annotation workflows** seamlessly into your existing pipelines.
- **Build custom AI models** leveraging accurately annotated data to maximize performance.
[**Contact us today**](/schedule-discovery-call) and streamline your annotation process with expert guidance.
### **References:**
1. Label Studio Data Annotation. \[Online\]. Available: [https://labelstud.io/](https://labelstud.io/)
2. Labelbox Data Annotation. \[Online\]. Available: [https://labelbox.com/product/annotate/](https://labelbox.com/product/annotate/)
3. Labelbox Example. \[Online\]. Available: [https://labelbox.com/customers/dialpad-customer-story/](https://labelbox.com/customers/dialpad-customer-story/)
4. AWS Sagemaker GroundTruth Data Annotation. \[Online\]. Available: [https://aws.amazon.com/sagemaker/groundtruth/](https://aws.amazon.com/sagemaker/groundtruth/)
5. AWS Sagemaker GroundTruth Example. \[Online\]. Available: [https://aws.amazon.com/blogs/machine-learning/helmet-detection-error-analysis-in-football-videos-using-amazon-sagemaker/](https://aws.amazon.com/blogs/machine-learning/helmet-detection-error-analysis-in-football-videos-using-amazon-sagemaker/)
6. Scale AI Data Annotation. \[Online\]. Available: [https://scale.com/data-engine](https://scale.com/data-engine)
7. Scale AI Example. \[Online\]. Available: [https://scale.com/customers/toyota](https://scale.com/customers/toyota)
### AI Training Data: What is it? How to build it?
URL: https://www.mycustomai.io/blog/ai-training-data-what-is-it-how-to-build-it
Summary: Effective strategies for assembling high-quality AI training data that powers progress in various domains, from healthcare to technology.
## **Introduction:**
The effectiveness of models hinges greatly on the quality and quantity of the data they are trained on. This article delves into the critical aspects of training data, exploring different approaches for its acquisition and the trade-offs associated with each. From human annotation to leveraging existing datasets and synthetic data generation, each approach offers unique advantages and challenges. By understanding these approaches and their implications, practitioners can strategically navigate the complexities of training data acquisition, optimizing the performance and reliability of their machine learning models in real-world applications.
| Approach | Cost | Quality | Quantity |
| --- | --- | --- | --- |
| Human Annotation | High | High | Low |
| Existing Datasets | Medium | Variable | Variable |
| Synthetic Data Generation | Low | Medium | High |
### **1\. What is training data?**
Training data is the foundation of [machine learning models](/what-is-custom-ai), consisting of input features paired with corresponding output labels or target values. These pairs serve as examples for the model to learn from during training. Input features represent data characteristics, while output labels indicate correct predictions. By exposing the model to diverse examples, it learns patterns and relationships, enabling accurate predictions on new data. In essence, training data teaches the model to understand and generalize from the underlying data distribution, enhancing its performance in real-world applications.
#### **1.1. Understanding Training Data: Different Types for Different Tasks**
Training data comes in many forms, each with its strengths and purposes in machine learning. Here's a breakdown of some common types:
- **Structured Data:** This type of data is organized into a tabular format with predefined columns and data types, making it easy to store and analyze. Examples include databases, spreadsheets, and CSV files.
- **Unstructured Data:** Unstructured data lacks a predefined structure and can include text, images, audio, and video. Unlike structured data, unstructured data does not fit neatly into rows and columns, presenting challenges for analysis and processing.
- **Labeled Data:** In labeled data, each example is paired with the correct label or target value, essential for supervised learning tasks where the model learns to map input features to output labels.
- **Unlabeled Data:** Unlabeled data only provides input features without corresponding labels. This type of data is commonly used in unsupervised learning tasks where the model must identify patterns or structures in the data without explicit guidance.
- **Imbalanced Data:** Imbalanced data refers to a dataset where the distribution of classes or labels is skewed, with some classes being more prevalent than others. Handling imbalanced data is crucial to prevent bias in the trained model and ensure fair predictions across all classes.
- **Synthetic Data:** Synthetic data consists of artificially generated data samples that mimic the characteristics of real-world data. It can be used to augment the training dataset, providing additional examples to improve model performance, especially when real data is scarce or expensive to obtain.
#### **1.2. How Training Data Impacts Performance**
Good training data is essential for a machine learning model to accurately capture underlying patterns and relationships within the data, resulting in robust predictions on unseen data. High-quality training data, representative of real-world scenarios, with accurate labels or annotations, enables the model to generalize well to new examples, enhancing its reliability in real-world applications.
Conversely, bad training data can lead to poor model performance and generalization. Data errors, inconsistencies, or biases can distort the learning process, resulting in inaccurate predictions. Issues such as missing values, incorrect labels, or noisy features contribute to model instability, leading to overfitting or underfitting. Careful curation and preprocessing of training data are essential to ensure model reliability and performance.
### **2\. Different approaches for Collecting Data:**
#### **2.1. Human Annotation:**
##### **2.1.1. Description:**
Human annotation involves manually labeling or annotating data examples by humans. This process is essential for tasks requiring subjective interpretation or when labeled data is not readily available.
##### **2.1.2. Characteristics:**
- **High Quality:** Human annotation ensures high-quality labeled data, as humans can understand context, nuances, and subtle patterns in data that automated methods might miss.
- **High Cost and Low Quantity:** Human annotation can be expensive and time-consuming due to the labor-intensive nature of the process. As a result, the quantity of annotated data may be limited by budget and time constraints.
##### **2.1.3. When to Use:**
Human annotation is needed when the task requires subjective interpretation or when labeled data with high-quality annotations is essential for model performance, such as in medical diagnosis or legal document analysis.****
##### **2.1.4. Example:**
Platforms like [Scale AI](https://www.scaleai.ca/training/) and [Labelbox](https://labelbox.com/product/annotate/) offer robust solutions for human annotation, where skilled annotators meticulously label data, ensuring high-quality annotations vital for machine learning tasks. Additionally, crowdsourcing platforms such as [Amazon Mechanical Turk](https://www.mturk.com/) provide scalable options for human annotation, enabling the efficient completion of large-scale labeling projects. Through human annotation, raw data is transformed into labeled datasets, empowering machine learning practitioners to develop models with enhanced accuracy and generalization capabilities across various domains.
#### **2.2. Existing Datasets:**
##### **2.2.1. Description:**
Existing datasets refer to publicly available or proprietary datasets that have been collected and annotated for specific tasks. These datasets serve as valuable resources for training and benchmarking machine learning models.
##### **2.2.2. Characteristics:**
- **Uncertain Quality:** The quality of existing datasets may vary, depending on factors such as data collection methods, annotation standards, and data preprocessing techniques.
- **Medium Cost and Low Quantity:** While existing datasets are generally more cost-effective than manual annotation, acquiring proprietary datasets or accessing high-quality public datasets may still incur expenses. Additionally, the quantity of data available in existing datasets may be limited for niche or specialized tasks.****
##### **2.2.3. When to Use:**
Existing datasets are needed when there is a need for a starting point for model development, especially in cases where manual annotation is impractical or time-consuming. They provide a foundation for training and benchmarking machine learning models.
##### 2.2.4. **Example:**
Platforms such as [Hugging Face](https://huggingface.co/datasets) and [Kaggle](https://www.kaggle.com/datasets) serve as invaluable hubs for accessing a diverse array of meticulously curated datasets, spanning from image classification to natural language processing tasks. These repositories provide researchers and practitioners with a rich resource pool for model development and experimentation, fostering innovation and collaboration within the machine learning community.
Furthermore, repositories like the [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets) and [TensorFlow Datasets](https://www.tensorflow.org/datasets) offer extensive collections covering various domains, further enhancing the accessibility of datasets for research and collaboration. Such repositories play a pivotal role in democratizing machine learning by facilitating access to high-quality data, thereby accelerating progress and driving advancements in the field.
#### **2.3. Synthetic Data Generation:**
##### **2.3.1. Description:**
Synthetic data generation involves creating artificial data examples that mimic real-world data. This approach is particularly useful for tasks where collecting real data is challenging or impractical.
##### **2.3.2. Characteristics:**
- **Medium Quality:** Synthetic data may exhibit slightly lower quality compared to real data, as it is generated based on statistical models or simulation techniques. However, with careful modeling and validation, synthetic data can closely resemble real data.
- **Low Cost and High Quantity:** Synthetic data generation is cost-effective and scalable, as it does not require manual annotation or data acquisition. Large volumes of synthetic data can be generated quickly and inexpensively, facilitating the training of robust machine learning models.
##### **2.3.3. When to use:**
Synthetic data generation is needed when there is a scarcity of labeled data or when additional data samples are required to enhance model performance. It can also be useful for generating diverse datasets that cover a wide range of scenarios or edge cases.
##### **2.3.2. Example:**
****Cutting-edge innovations like [Phi-1](https://arxiv.org/pdf/2306.11644) exemplify the power of the teacher-student model paradigm. This sophisticated approach involves initially training a teacher model on authentic data, which subsequently generates synthetic data based on its learned patterns and insights. These synthetic datasets serve as invaluable resources for training a student model, allowing it to learn from a wider range of examples and scenarios than what may be available in the original training data alone. By leveraging synthetic data in this manner, the student model can achieve enhanced performance and adaptability across various tasks and domains. However, it's crucial to acknowledge potential challenges such as biases in the synthetic data and the necessity for rigorous validation and testing to ensure the robustness and reliability of the trained student model.
### **3\. Strategic Approach to Training Data Acquisition**
Strategic training data acquisition is vital for success in building custom AI models for companies. Our approach emphasizes a systematic sequence: starting with existing datasets, followed by synthetic data generation, and concluding with human annotation. This sequence optimizes resource allocation, minimizes costs, and improves data diversity and quality. Factors such as data availability, cost-effectiveness, and quality guide each step. This approach aims to streamline data acquisition, enhancing AI model performance in real-world applications.
Existing Datasets
Synthetic Data Generation
Human Annotation
#### **Practical Example:**
Imagine you're building an AI model to analyze customer sentiment in social media reviews. Here's how this approach would work:
1. **Existing Datasets:** Start by searching for publicly available datasets of social media reviews. Platforms like Kaggle offer datasets categorized by sentiment (positive, negative, neutral). This provides a foundation for your model.
2. **Synthetic Data Generation:** Since social media reviews often contain private information, synthetic data generation can be used to create additional data points that mimic real reviews while protecting privacy. This expands your training data without privacy concerns.
3. **Human Annotation:** Finally, a smaller sample of real customer reviews can be manually annotated by human experts to identify specific emotions or nuances not captured by synthetic data. This refines your model's understanding of sentiment.
By following this staged approach, you leverage the strengths of each data acquisition method, building a cost-effective, diverse, and high-quality training dataset for your AI model.
### **Conclusion:**
In the realm of machine learning, training data acts as the cornerstone for building effective models. It's not just about quantity; quality matters too. By strategically selecting and curating training data through approaches like human annotation, leveraging existing datasets, and synthetic data generation, we lay a solid foundation for our models to learn and generalize effectively. This ensures that our AI systems are not only accurate but also adaptable to real-world challenges. By prioritizing thoughtful data acquisition, we unlock the full potential of artificial intelligence to drive innovation and solve complex problems across various domains.
### **References:**
1. Hugging Face Datasets. \[Online\]. Available: [https://huggingface.co/datasets](https://huggingface.co/datasets).
2. Kaggle Datasets. \[Online\]. Available: [https://www.kaggle.com/datasets](https://www.kaggle.com/datasets)
3. UCI Machine Learning Repository. \[Online\]. Available: [https://archive.ics.uci.edu/datasets](https://archive.ics.uci.edu/datasets)
4. TensorFlow Datasets. \[Online\]. Available: [https://www.tensorflow.org/datasets](https://www.tensorflow.org/datasets)
5. Gunasekar, S., Zhang, Y., et al. (2023). \*Textbooks Are All You Need.\* Retrieved from Microsoft Research. \[Online\]. Available: [https://arxiv.org/pdf/2306.11644](https://arxiv.org/pdf/2306.11644)
### Unlocking Productivity: Custom AI Models for Code Generation
URL: https://www.mycustomai.io/blog/code-generation-for-software-development
Summary: We revolutionized software development productivity by developing a tailored AI solution through a meticulous five-step process. Our custom AI model improved team velocity, increased test coverage, and empowered our client's team members, resulting in enhanced capabilities and customer satisfaction, demonstrating the value and competitive advantage of custom AI in addressing specific business needs.
### Context
We aim to address the limitations of existing AI powered code generation solutions, such as accuracy for specific programming frameworks, privacy concerns and cost. By leveraging our expertise and embracing the potential of Custom AI, we adopt a white glove process comprising five core steps. This case study illustrates our systematic approach leading to enhanced software development productivity, improved in-house capabilities, and expanded business opportunities.
### Our approach
1 - Scope Code Completion 2 - Prototype a programming framework 3 - Deployment Specific team \=>increase velocity 4 - Improvement Performance monitoring Productivity increase & Expansion to other use cases 5 - Empowerment Training & New processes
Scope
(Dynamic Workflow AI Agent)
Prototype
(Customer support for bank transfers)
Deployment
(US east region => increase in C-sat)
Improvement
(Performance monitoring)
Empowerment
(Training & New processes)
Productivity Increase
(Expansion to other use cases)
Empowerment
(Training & New processes)
Productivity Increase
(Expansion to other use cases)
#### Step 1: Scope
The initial phase involved enumerating various use cases and prioritizing them based on their significance. The most prominent use case identified was to create a private code completion IDE plugin for large and complex codebase to adapt an existing and specific in-house programming code-style.
#### Step 2: Prototype
We decided to concentrate on one specific use case: code completion for one particular programming framework. To measure the success of the custom AI model, we defined key business metrics with the client like team velocity and test coverage. The company collected relevant training data (screenshot) to train their custom AI model. Additionally, we conducted benchmark tests to validate the potential improvement over existing solutions. A series of A/B tests were performed to compare the performance of the custom AI model with other existing approaches, ensuring its viability and efficacy.
#### Step 3: Deployment
Upon achieving positive results during the prototype phase, we moved forward to deploy the custom AI model into production for the initial use case. Collaborating with our client, we identified an appropriate Operations Infrastructure (Ops Infra) that aligned with their requirements. The model was deployed for a specific internal software engineering team with huge feature backlog. Backtesting was conducted to validate the impact of the custom AI model and measure the resulting increase in the team velocity.
#### Step 4: Improvement
With the model deployed in production, the company proactively monitored its performance in the implemented use case. We analyzed the gathered data, user feedback, and other relevant metrics to identify areas of improvement. Simultaneously, we expanded the application to other code generations use cases, such as creating unit testing to increase the test coverage and reduce regressions. The process for these additional use cases followed the same steps as in the prototype and deployment phases, ensuring consistency and replicability.
#### Step 5: Training and Team Empowerment
We provided necessary training to bring everyone up to speed on the new tools to understand the new capabilities they offer as well as their limitations and how they could improve them. We also helped software engineer managers and product managers adapt their processes given the new tools.
### Outcome
The implementation of the custom AI model resulted in a significant improvement in the company productivity. By addressing the limitations of existing solutions and developing tailored AI solutions, the client was satisfied as well as the teams using the tools. Moreover, the project led to an enhancement of in-house capabilities, with team members being able to focus on other high value added tasks.
Through a meticulous white glove process comprising five core steps, we successfully developed and deployed a custom AI model for customer support. By prioritizing use cases, prototyping, deploying, improving, and empowering our client’s team members, the company enhanced customer satisfaction and improved internal capabilities. This case study exemplifies the value of custom AI in addressing specific business needs and illustrates the potential for companies to leverage AI technology to gain a competitive advantage in their respective industries.
### Comparative of Commercial Large Language Models (LLMS)
URL: https://www.mycustomai.io/blog/comparative-of-commercial-large-language-models-llms
Summary: Discover our guide on ChatGPT, Cohere, Anthropic, Mistral, and Gemini, analyzing their core features, pricing models, and potential impact on your business operations.
### 0\. Introduction
Large language models (LLMs) are pivotal for diverse applications in the rapidly developing AI field, enhancing efficiency and innovation. This guide explores the top LLM programs—ChatGPT, Cohere, Anthropic, Mistral, and Gemini(formerly Bard -Google)—highlighting their functionalities, costs, and unique attributes. Our comparative analysis offers a foundational understanding to assist in selecting the appropriate LLM tailored to specific operational requirements.
### 1\. ChatGPT
ChatGPT is a language model developed from GPT-3.5, enhanced for conversational capabilities through Reinforcement Learning with Human Feedback (RLHF). This process involves guiding the model towards preferred responses using human input. For more detailed information, visit [here](https://help.openai.com/en/articles/6783457-what-is-chatgpt).
#### 1.1 Features
1. **Conversational Understanding and Generation:** ChatGPT stands out with its unique ability to understand context and generate human-like responses. This makes it a powerful tool for holding conversations across various topics. Its strength lies in its foundational training on diverse text data, which allows it to comprehend and participate in discussions with a natural flow.For in-depth discussions on ChatGPT's conversational abilities, refer to OpenAI's general introduction and updates on ChatGPT: [OpenAI ChatGPT: Optimizing Language Models for Dialogue](https://openai.com/blog/chatgpt).
2. **Reinforcement Learning with Human Feedback (RLHF):** A significant enhancement in ChatGPT is using RLHF, a training approach that refines the model's responses based on preferences indicated by human feedback. This method helps improve the generated text's quality, relevance, and safety, making the interactions more user-friendly and aligned with desired outcomes. For detailed insights into the Reinforcement Learning with Human Feedback (RLHF) process used in enhancing ChatGPT, refer to the paper "Fine-Tuning Language Models from Human Preferences" available on [arXiv](https://arxiv.org/pdf/1909.08593.pdf).
3. **Multimodal Capabilities:** Although primarily focused on text, newer ChatGPT(GPT-4) versions have multimodal capabilities. This means they can understand and generate information across different formats, such as images and audio. This feature broadens the application scope from simple text-based tasks to more complex, media-rich interactions. OpenAI's research on multimodal models offers insights into integrating various data types: [OpenAI DALL·E: Creating Images from Text](https://openai.com/blog/dall-e).
4. **Language and Domain Adaptability:** ChatGPT can adapt well to various languages and domains thanks to its extensive training data. This feature enables it to cater to a global audience and perform tasks across different fields, from casual conversations to technical discussions, without extensive customization. Their research and model descriptions [document](https://arxiv.org/pdf/2005.14165.pdf) OpenAI's approach to creating versatile language models that can adapt to various languages and domains.
5. **Continuous Learning and Updating:** OpenAI's commitment to continuous learning and updating ChatGPT models ensures they are always up-to-date with the latest information and trends. This ongoing development enhances their performance, knowledge, and safety features, making ChatGPT a versatile tool for current and future applications.For updates on the latest improvements and versions of ChatGPT, including how OpenAI incorporates new data and feedback, visit the OpenAI blog and specifically look for posts related to ChatGPT updates: [OpenAI Blog](https://openai.com/blog/).
### 2\. Cohere
Cohere's Retrieval Augmented Generation (RAG) toolkit enhances Large Language Models (LLMs) by integrating enterprise data as the primary source for answers and solutions. RAG ensures accuracy and relevance in the models' outputs by pulling relevant information during the question-answering process. This approach combines the generative capabilities of LLMs with precise, data-backed responses, offering a more effective solution for tasks requiring specific, factual information.
#### 2.1 Features
1. **Chat with RAG:** Cohere's Command enables the creation of powerful AI Agents and knowledge assistants using Retrieval Augmented Generation, leveraging enterprise data for accurate conversations.
2. **Powerful, Accurate Semantic Search:** The Embed model by Cohere allows for the construction of potent search solutions, offering high performance in English and over 100 other languages for relevant search results.
3. **Search Performance Improvement:** Cohere's Rerank improves the relevance of search results from existing tools, which are customizable by domain for enhanced performance.
4. **Customizable Models:** Cohere provides sophisticated customization tools for superior model performance at reduced inference costs, enabling fine-tuning capabilities.
5. **Flexible Deployment Options:** Cohere offers models through SaaS API, cloud services (e.g., OCI, AWS SageMaker, Bedrock), and private deployments (VPC and on-prem), ensuring deployment versatility.
Visit Cohere's official [website](https://cohere.com/) for more detailed information on each feature.
### 3\. Anthropic
Anthropic is a cutting-edge AI company that develops sophisticated large language models (LLMs) and AI Agents. Claude is its most notable product to date. Similar to ChatGPT, Claude is designed to offer advanced conversational capabilities, showcasing Anthropic's commitment to building AI systems that are safe, reliable, and highly interactive. For more information about the company and its innovative work in AI, visit [Anthropic's website](https://www.anthropic.com/).
#### 3.1 Features
1. **Improved Accuracy and Trustworthiness:** Claude exhibits a significant improvement in providing accurate and reliable responses, especially with complex, factual questions. The models also aim to reduce incorrect answers and offer the capability to cite sources to verify answers.
2. **Long Context and Near-Perfect Recall:** The Claude models have been designed to handle extended inputs, initially offering a 200K token context window, with capabilities to process inputs exceeding 1 million tokens for certain customers, ensuring effective processing of long context prompts and demonstrating near-perfect recall in information retrieval.
3. **Responsible Design:** Anthropic focuses on creating trustworthy AI by addressing risks ranging from misinformation to privacy issues and continuously working to reduce biases in the models to ensure neutrality and safety.
4. **Ease of Use:** Claude is designed to be user-friendly, excelling in following complex, multi-step instructions and adhering to specific response guidelines or brand voices, making it more straightforward for developers and businesses to utilize for various applications.
5. **Multilingual Capabilities and Vision Processing:** The Claude models offer improved fluency in multiple non-English languages and can process visual inputs, making them versatile for global use cases and applications that require image analysis.
Learn more about these features [here.](https://www.anthropic.com/news/claude-3-family)
### 4\. Mistral AI
Mistral AI offers a comprehensive approach to utilizing Large Language Models with options like a pay-as-you-go API, cloud-based deployments, and open-source models under the Apache 2.0 License, ensuring flexibility and accessibility for all levels of users. For more details, visit [Mistral AI's documentation](https://docs.mistral.ai/).
#### 4.1 Features
1. **Frontier Performance:** Mistral AI models are designed for unmatched latency-to-performance ratios, excelling in top-tier reasoning across all standard benchmarks. The focus is on creating unbiased, applicable models with complete modular control over moderation.
2. **Open and Portable Technology:** Emphasizing the power of open technology, Mistral AI provides competent models under fully permissive licenses, aiming to accelerate AI innovation. The platform ensures customer independence by offering portable solutions across different clouds and infrastructures.
3. **Flexible Deployment Options:** Mistral AI supports optimized model deployment tailored to specific needs, whether close to the data source or within required security parameters, maintaining application hermeticity.
4. **Customization:** Offering unique levels of customization and control, Mistral AI enables full fine-tuning capabilities, allowing seamless integration of models with business systems and data.
5. **Comprehensive API Access:** Mistral AI's platform offers versatile API access, including pay-as-you-go for the latest models, cloud-based deployments, and access to open-source models under the Apache 2.0 License. This wide range of access options caters to various user needs, from individual developers to large enterprises.
For an in-depth exploration of Mistral AI's innovative features and how they can transform your projects with state-of-the-art AI technology, visit [Mistral AI's official website](https://mistral.ai/).
### 5\. Gemini
Gemini is Google AI's next-generation family of large language models (LLMs), launched in December 2023. Gemini aspires to be Google's most capable AI model yet. Optimized in three versions - Ultra, Pro, and Nano - it promises unparalleled flexibility and performance across devices.
#### 5.1 Features
1. **Multimodal Capabilities:** Gemini is designed to understand and integrate different types of information, including text, images, audio, and video, seamlessly.
2. **Optimized Versions:** There are three optimized versions for various applications: Gemini Ultra for complex tasks, Gemini Pro for a range of tasks, and Gemini Nano for on-device tasks.
3. **State-of-the-Art Performance:** Gemini Ultra outperforms human experts on MMLU (Massive Multitask Language Understanding), showcasing exceptional reasoning and problem-solving abilities.
4. **Sophisticated Reasoning:** Gemini's advanced reasoning capabilities allow it to process complex written and visual information, making it highly effective in knowledge discovery.
5. **Efficiency and Scalability:** It runs significantly faster on Google's Tensor Processing Units (TPUs), demonstrating reliability and scalability for training and serving.
For more in-depth information, visit [here.](https://blog.google/technology/ai/google-gemini-ai/)
### 6\. Pricing
#### 6.1 ChatGPT
- **Free Plan:** Access to GPT-3.5 with unlimited messages, interactions, and history. Available on the web, iOS, and Android.
- **Plus Plan:** $20 per month for access to GPT-4 and additional tools like DALL·E, browsing, advanced data analysis, and more.
- **Team Plan:** $25 per user/month billed annually, or $30 per user/month billed monthly. Offers higher message caps and team management features.
- **Enterprise Plan:** Custom pricing with unlimited high-speed access to GPT-4, expanded context window, and priority support. [Source](https://openai.com/chatgpt/pricing).
#### 6.2 Cohere
- Free Plan: Rate-limited access for learning and prototyping, including all endpoints and ticket support.
- Production: Pay-as-you-go pricing with $1.00 per 1M input tokens and $2.00 per 1M output tokens. Customizable options for businesses.
- Enterprise Plan: Custom solutions with dedicated model instances and support. [Source.](https://cohere.com/pricing)
#### 6.3 Anthropic (Claude)
- Claude 3 offers three pricing tiers. Haiku for light and fast tasks at $0.25 input/$1.25 output per million tokens (MTok), Sonnet for hard-working applications at $3 input/$15 output per MTok, and Opus for powerful needs at $15 input/$75 output per MTok. Each supports the vision and a 200,000 token context window.
For comprehensive details on pricing and options, visit [Anthropic's API pricing page](https://www.anthropic.com/api#pricing).
#### 6.5 Mistral
- Mistral AI offers competitive pricing for its Chat Completions API, with the light and fast "Mistral 7B" model starting at $0.25 per MTok for input and the same for output. The "Mistral 8x7B" model costs $0.75 per MTok for both input and output. There are also different tiers like "Mistral Small," "Medium," and "Large" for various levels of performance and pricing, tailored to suit different needs and budgets. For detailed pricing options, refer to the Mistral AI pricing page [here](https://docs.mistral.ai/platform/pricing/).
#### 6.6 Gemini (Google)
- Gemini AI offers two pricing structures: a free tier with rate limits of 60 queries per minute for both input and output and a pay-as-you-go tier with pricing as follows: $0.000125 per 1K characters for input and $0.000375 per 1K characters for output. Images are priced at $0.0025 each for input. For more detailed pricing information and to explore additional services and offerings, visit the [Google AI pricing page](https://ai.google.dev/pricing).
### 7\. MyCustomAI: A White Glove Approach
MyCustomAI employs a meticulous approach to AI development, delivering highly customized solutions that align closely with each business's unique requirements. This process involves:
- **Strategic Customization:** Adapting AI models to meet specific business goals, exceeding standard AI functionalities.
- **Continuous Optimization:** Leveraging ongoing performance assessments to ensure AI solutions evolve in line with business objectives, fostering consistent enhancement.
This methodology demonstrates a commitment to utilizing AI's transformative potential by creating customized AI strategies that address specific business scenarios.
### 8\. Conclusion
The comprehensive review of leading Large Language Models highlights the significance of selecting an LLM that aligns with specific business requirements. It showcases the diversity and capabilities of ChatGPT, Cohere, Anthropic, Mistral, and Gemini, along with the bespoke solutions offered by MyCustomAI. This analysis is crucial for businesses aiming to integrate AI technologies that complement and enhance their operational strategies and objectives, underscoring the pivotal role of tailored AI in achieving technological and competitive advancement.
### 9\. References
1. Ziegler, D. M., et al. (2020, January). _Fine-Tuning Language Models from Human Preferences_. Retrieved from [arXiv:1909.08593](https://arxiv.org/pdf/1909.08593.pdf).
2. Brown, T. B., et al. (2020, July 22). _Language Models are Few-Shot Learners._ Retrieved from [arXiv:2005.14165](https://arxiv.org/pdf/2005.14165.pdf).
3. OpenAI. (n.d.). _What is ChatGPT?_ Retrieved from [OpenAI Help Center](https://help.openai.com/en/articles/6783457-what-is-chatgpt).
4. OpenAI. (n.d.). _Introducing ChatGPT_. Retrieved from [OpenAI Blog](https://openai.com/blog/chatgpt).
5. OpenAI. (n.d.). _DALL·E: Creating images from text_. Retrieved from [OpenAI Research](https://openai.com/research/dall-e).
6. Cohere. (n.d.). _Build conversational apps with RAG_. Retrieved from [Cohere](https://cohere.com/)
7. Anthropic. (n.d.). _AI research and products that put safety at the frontier._ Retrieved from [Anthropic](https://www.anthropic.com/).
8. Anthropic. (2024, March 4). Introducing the next generation of Claude. Retrieved from [Anthropic](https://www.anthropic.com/news/claude-3-family).
9. Mistral AI. (n.d.). Introduction. Retrieved from [Mistral AI Documentation](https://docs.mistral.ai/).
10. Google AI. (n.d.). _Introducing Gemini: our largest and most capable AI model._ Retrieved from [Google AI Blog](https://blog.google/technology/ai/google-gemini-ai/).
### Custom AI beyond LLMs: Vision, Audio, Multimodal
URL: https://www.mycustomai.io/blog/custom-ai-beyond-llms-vision-audio-multimodal
Summary: Beyond LLMs: Custom AI in Vision, Audio, and Multimodal Systems
## **Introduction**
Custom AI solutions extend beyond language models, encompassing advanced vision, audio, and multimodal technology capabilities. These diverse AI models offer unique adaptability, showcasing innovation in fields that transcend traditional text analysis. From sophisticated image generation to intricate audio processing, the scope of customizable AI is vast and full of untapped potential.
### **Type of AI Models**
#### **Text**
Text AI models are designed to understand, interpret, and generate human language. They are used in AI Agents, content generation, and language translation applications.
**Commercial Example - ChatGPT**, developed by OpenAI, is a conversational AI model for generating human-like text responses.
**Open Source Example - Mistral** offers open-weight models like [Mixtral 8x7B](https://mistral.ai/news/mixtral-of-experts/), known for their efficiency and adaptability in various use cases, supporting multiple languages and coding abilities.
#### **Vision**
Vision AI models specialize in interpreting and generating visual content. They are crucial in applications like image generation, enhancement, and analysis, using deep learning to understand and recreate visual elements from given data inputs.
**Commercial Example - DALL-E,** developed by OpenAI, represents a significant advancement in vision AI. It's a text-to-image model capable of creating complex and creative images from textual descriptions. This model exemplifies the commercial application of vision AI in creative and design fields.
**Open Source Example - Stable Diffusion** is a notable open-source counterpart in vision AI. Stable Diffusion is particularly noteworthy for its ability to operate in [latent space](https://ommer-lab.com/research/latent-diffusion-models/), which allows for the high-quality and flexible generation of images conditioned on various inputs like text or bounding boxes. It stands out for its efficiency and ability to run on consumer-grade GPUs. Trained on a diverse dataset, Stable Diffusion can generate detailed and varied images from textual prompts. One of its key features is the flexibility of end-users to fine-tune the model for specific use cases, such as generating personalized or stylistically unique images. The model has been developed to emphasize computational and memory efficiency, making it accessible to many users and applications.
#### **Audio**
Audio AI models, particularly in speech-to-text, are pivotal in transforming spoken language into written text. They are widely used for transcriptions, voice-controlled applications, and accessibility tools.
**Commercial Example - AWS Transcribe**: a service by Amazon Web Services, offers advanced speech recognition capabilities, allowing for the transcription of audio files into text. It's designed for high accuracy and can handle various accents and languages. For more information, visit [AWS Transcribe](https://aws.amazon.com/pm/transcribe).
**Open Source Example - Whisper**, Developed by OpenAI, Whisper is an open-source speech recognition system known for its robust performance across multiple languages and contexts. It stands out for its adaptability and accuracy in different environments and applications. Further details can be found at [OpenAI Whisper](https://openai.com/research/whisper).
#### **Multimodal**
Multimodal AI models integrate and analyze data from multiple different modes or types of input, such as text, images, and audio. These models can understand and generate complex content that spans different forms of media.
**Commercial Example - GPT-4V**, represents a cutting-edge development in multimodal AI, offering capabilities that combine text and visual inputs for diverse applications.
**Open Source Example - LLaVA**, is an open-source multimodal AI model that showcases advancements in integrating vision and language processing. More information about LLava can be found at [LLava](https://llava-vl.github.io/).
#### **Video**
Video AI is a developing field that applies the principles of image AI to videos. These models are designed to interpret and generate video content, often analyzing data frame-by-frame.
The "**ViViT: A Video Vision Transforme**r" research paper is a key reference in this domain. This [study](https://arxiv.org/pdf/2103.15691.pdf) explores the use of transformer-based models for video classification, a method that has shown promising results, especially in handling spatiotemporal data efficiently.
The research emphasizes the effectiveness of these models even on comparatively small datasets, underscoring their potential in various video-related applications.
### **Two High-Level and Major Approaches**
Two major approaches take center stage when understanding the detailed workings of large language models (LLMs) like ChatGPT: Next Token Prediction and the Diffusion Approach.
#### **Next Token Prediction in AI Models**
Next Token prediction is a core mechanism in many AI models, particularly language models. It involves predicting the subsequent word or token in a sequence based on the context provided by the preceding tokens. This technique is fundamental in generating coherent and contextually relevant text in models like ChatGPT.
For a more detailed exploration of next token prediction and its role in the mechanics of Language Models (LLMs), refer to our previous blog post, "[What are the mechanics inside LLM?](/blog/what-are-the-mechanics-inside-llm)"
##### **Token interpretation**
###### **1\. Text: Part of the Word**
Token interpretation in text-based AI involves analyzing segments of words rather than whole words. This approach allows for a deeper understanding of language nuances and syntax. It enables the AI to handle complex linguistic elements such as morphemes and compound words more effectively. This granular focus leads to more precise and contextually appropriate text generation, making these models highly adaptable to the intricacies of human language.
###### **2\. Image: Pixel**
Image-based AI models interpret each pixel as a token, especially in multimodal and open-source platforms. These models predict the next pixel using data from surrounding pixels, much like text-based models predict the next word segment. This pixel-focused approach allows AI to generate detailed images precisely, playing a crucial role in image generation and enhancement tasks.
###### **3\. Multimodal**
_1.1._ [LLaVA network architecture](https://arxiv.org/pdf/2304.08485.pdf)
Multimodal AI models synthesize information from text and images, interpreting words and pixels together to generate responses. The attached figure shows that these models integrate visual data encoded by a vision encoder with textual instructions processed by a language model.
The combined data is then used to produce a language response that accurately reflects the image's content, context, and associated text. This integrated approach allows multimodal AI to understand and respond to complex queries that require an analysis of visual elements alongside textual data.
#### **Diffusion Approach**
The diffusion approach in AI, exemplified by models like Midjourney and DALLE, represents a generative technique that progressively refines images from random noise to detailed visuals. For an in-depth look into the latent diffusion models that underpin this process, further details are available on the following [research page](https://ommer-lab.com/research/latent-diffusion-models/).
### **Conclusion**
The potential to personalize AI goes beyond language and can be applied to various domains. Vision, audio, and multimodal AI models demonstrate an extraordinary capacity for adaptation, driven by underlying mechanisms that are remarkably similar across different modalities.
Text AI models dissect language to its smallest units, offering nuanced interpretations. Vision AI transforms pixel data into complex images, and audio AI converts sound waves into meaningful text. Multimodal AI, perhaps the most sophisticated of all, weaves words and visual elements together to respond intelligently to multifaceted inputs.
The same principles that allow us to tailor language models to specific needs also apply to vision, audio, and multimodal models. Whether it's through next token prediction or the diffusion approach, the customization techniques are fundamentally interconnected. By understanding these relationships and the shared techniques behind them, we are better equipped to push the boundaries of AI even further, framing solutions that are as diverse and dynamic as the challenges they aim to address.
### **References**
1. Liu, H._, Li, C._, Wu, Q._, & Lee, Y. J._ (2023). _Visual Instruction Tuning_. Retrieved from [arXiv:2304.08485v2](https://arxiv.org/pdf/2304.08485.pdf).
2. OpenAI. (2023). _Introducing Whisper_. Retrieved from [OpenAI](https://openai.com/research/whisper).
3. Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023). _LLaVA: Large Language and Vision Assistant. Visual Instruction Tuning._ NeurIPS 2023 (Oral). Retrieved from [https://llava-vl.github.io/](https://llava-vl.github.io/).
4. Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., et al. (2024). _Mixtral of Experts._ Retrieved from [arXiv:2401.04088v1](https://arxiv.org/pdf/2401.04088.pdf).
5. Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., & Schmid, C. (2021). _ViViT: A Video Vision Transformer_. Retrieved from [arXiv:2103.15691](https://arxiv.org/pdf/2103.15691.pdf).
### Taking the bank's customer support to the next level with custom AI solutions
URL: https://www.mycustomai.io/blog/customer-support
Summary: This case study highlights how MyCustomAI delivers value to companies' customer support. By addressing the limitations of existing solutions, such as reliability and privacy concerns, the company adopted a white glove process comprising five core steps. Through scoping, prototyping, deploying, improving, and empowering team members, we successfully developed and implemented a custom AI model for customer support, resulting in enhanced customer satisfaction and improved in-house capabilities. This case study demonstrates the effectiveness of leveraging Custom AI and showcases the potential for AI technology to revolutionize customer support experiences.
### Context
We aim to address the limitations of existing AI powered customer support solutions, such as reliability, privacy concerns, third-party dependencies, and the need for deep integration with internal software. By leveraging our expertise and embracing the potential of Custom AI, we adopt a white glove process comprising five core steps. This case study illustrates our systematic approach leading to enhanced customer satisfaction, improved in-house capabilities, and expanded business opportunities.
### Our approach
1 - Scope Dynamic workflow AI Agent 2 - Prototype Customer support for bank transfers 3 - Deployment US east region \=>increase in C-sat 4 - Improvement Performance monitoring C-Sat improvement & Expand to other use cases 5 - Empowerment Training & New processes
Scope
(Dynamic Workflow AI Agent)
Prototype
(Customer support for bank transfers)
Deployment
(US east region => increase in C-sat)
Improvement
(Performance monitoring)
Empowerment
(Training & New processes)
C-Sat Improvement
(Expand to other use cases)
Empowerment
(Training & New processes)
C-Sat Improvement
(Expand to other use cases)
#### Step 1: Scope
The initial phase involved enumerating various use cases and prioritizing them based on their significance. The most prominent use case identified was to create an AI Agent that allows for dynamic workflow.
#### Step 2: Prototype
We decided to concentrate on one specific use case: customer support for bank transfers. To measure the success of the custom AI model, we defined key business metrics with the client, like customer satisfaction (C-sat). The company collected relevant training data (screenshot) to train their custom AI model. Additionally, we conducted benchmark tests to validate the potential improvement over existing solutions. A series of A/B tests were performed to compare the performance of the custom AI model with other existing approaches, ensuring its viability and efficacy.
#### Step 3: Deployment
Upon achieving positive results during the prototype phase, we moved forward to deploy the custom AI model into production for the initial use case. Collaborating with our client, we identified an appropriate Operations Infrastructure (Ops Infra) that aligned with their requirements. The model was deployed in a specific market, initially targeting the US east region. Backtesting was conducted to validate the impact of the custom AI model and measure the resulting increase in customer satisfaction.
#### Step 4: Improvement
With the model deployed in production, the company proactively monitored its performance in the implemented use case. We analyzed the gathered data, user feedback, and other relevant metrics to identify areas of improvement. Simultaneously, we expanded the application to other customer support use cases, such as routing customers to the right support representatives. The process for these additional use cases followed the same steps as in the prototype and deployment phases, ensuring consistency and replicability.
#### Step 5: Training and Team Empowerment
We provided necessary training to bring everyone up to speed on the new tools to understand the new capabilities they offer as well as their limitations and how they could improve them. We also helped workforce managers adapt their processes given the new tools.
### Outcome
The implementation of the custom AI model resulted in a significant improvement in customer satisfaction. By addressing the limitations of existing solutions and developing tailored AI solutions, the client was satisfied as well as the teams using the tools. Moreover, the project led to an enhancement of in-house capabilities, with team members being able to focus on other high value added tasks.
Through a meticulous white glove process comprising five core steps, we successfully developed and deployed a custom AI model for customer support. By prioritizing use cases, prototyping, deploying, improving, and empowering our client’s team members, the company enhanced customer satisfaction and improved internal capabilities. This case study exemplifies the value of custom AI in addressing specific business needs and illustrates the potential for companies to leverage AI technology to gain a competitive advantage in their respective industries.
### Deciphering AI Strategies: The Executor and Leader Approaches
URL: https://www.mycustomai.io/blog/deciphering-ai-strategies-the-executor-and-leader-approaches
Summary: Compare two AI architecture strategies — the Executor approach for precision-driven data analysis and the Leader approach for interactive, real-time decision-making — with e-commerce search examples.
## Introduction
In the realm of artificial intelligence, our on-the-ground experience has crystallized two distinct approaches to system design and application: the AI Executor and the AI Leader. Emergent from the trenches of our project work, the Executor approach is a testament to systematism, ideal for tasks demanding high precision and extensive data analysis. In contrast, the Leader approach is born of the need for interactivity and adaptability, thriving in dynamic environments that require agile responses. This post draws on our hands-on experience to delve into these strategies, offering insights to inform the strategic deployment of AI in your business operations.
## **The AI Executor Approach**
#### **Definition and Mechanics**
The AI Executor approach designates AI as a task processor, which activates upon specific external prompts rather than initiating interaction. In this model, user queries are first fielded by data retrieval systems, which fetch relevant information from expansive datasets. The AI steps in post-retrieval, applying its computational might to analyze, synthesize, or summarize the fetched data, tailored to the user’s request.
#### **Use Cases**
This approach excels in structured searches and data-heavy tasks where precision is paramount—think legal analyses, market research, and complex data mining. It's particularly adept at converting extensive, unstructured data into coherent, actionable insights, in response to precise user queries.
#### **Pros and Cons**
Strengths of the AI Executor lie in its ability to contextualize data with precision, enhancing relevance and reducing informational noise. It also strategically manages latency by focusing AI on complex analysis rather than initial data retrieval. However, the approach may not fully leverage AI's interactive potential, offering limited dynamism in real-time responsiveness and potentially underusing AI's holistic capabilities.
## **The AI Leader Approach**
#### **Definition and Mechanics**
Drawing from the wellspring of practical experience in deploying AI solutions, the AI Leader approach emerges as a proactive and interactive framework. In this paradigm, AI takes the helm, directly engaging with users and autonomously orchestrating the flow of information across various system components. When a user interacts with an AI Leader system, the AI not only processes the input but also determines what additional data is needed, how to retrieve it, and the best way to utilize it to achieve the desired outcome. This process underscores the AI's role as a decision-maker, a navigator through the complexity of large data landscapes, prioritizing tasks and directing data flow in real-time.
#### **Use Cases**
The AI Leader approach is particularly advantageous in environments where user interaction is continuous and the context is evolving, such as in personalized recommendations, interactive AI Agents, and adaptive learning platforms. It's designed to handle fluid scenarios, where user inputs can vary widely and the system must be adept at interpreting and reacting to nuanced requests on the fly.
#### **Pros and Cons**
One of the main advantages of the AI Leader approach is its dynamic nature. It can provide real-time responses and adapt to the changing context of user interactions, which enhances user engagement and satisfaction. It also has the capability to learn from each interaction, becoming more attuned and responsive to user needs over time.
However, its real-time decision-making capability can also be a double-edged sword. The complexity of managing multiple components and the need for the AI to understand and act upon a wide array of inputs can result in higher computational demands and potentially slower response times if not managed carefully. Furthermore, it may require more sophisticated development and maintenance to ensure the AI Leader can handle the breadth of tasks it's responsible for.
## **Real-World Application: E-commerce Search**
#### **The AI Executor Approach in E-commerce Search**
In the e-commerce domain, the AI Executor approach can be exemplified through advanced search query understanding. The goal is to allow users to input queries of varying specificity and still yield highly relevant results. For example, a general search query like "blue running shoes" would return a broad range of products fitting that description. In contrast, a more specific query such as "men's blue Adidas running shoes size 11" would result in a narrow, targeted selection of products. This granularity in search results hinges on the system's ability to semantically understand and differentiate the queries.
###### **Pros and Cons**
_Pros:_
- Enhanced Relevance: Semantic understanding ensures that search results closely align with the user's intent, whether broad or specific.
- Precision: The AI Executor is adept at distinguishing between the general and the specific, delivering accurate results tailored to the query.
_Cons:_
- Limited Interaction: The system may not engage the user beyond the search, missing opportunities to guide or upsell.
- Static Experience: Without dynamic interaction, the search experience remains transactional rather than conversational.
#### **The AI Leader Approach in Interactive Customer Support**
An AI Leader approach is well-suited for an interactive customer support AI Agent within an e-commerce platform. Here, the AI takes an active role in conversing with the customer, capable of understanding complex requests and performing tasks such as locating resources, redirecting to human support, or providing real-time quotes. For instance, a customer might ask, "What are the best running shoes for marathons?" and the AI, through a series of interactive questions, could guide them to products, offer personalized suggestions, or connect them with a running expert.
###### **Pros and Cons**
_Pros:_
- Dynamic Interaction: The AI can lead a conversation, react to user inputs in real-time, and provide a personalized experience.
- Multifaceted Utility: Beyond search, it can perform a range of services like quotes and suggestions, enhancing customer service.
_Cons:_
- Complexity: The system's need to understand and manage multiple tasks can lead to complex AI models that are challenging to maintain.
- Computational Demand: Real-time processing and decision-making can strain computational resources, affecting response times.
## **Conclusion: Navigating AI Strategy for Business Impact**
Selecting the right AI approach is pivotal. The AI Executor excels in precision and structured tasks, while the AI Leader is dynamic, ideal for interactive experiences. Key performance indicators such as computational efficiency, throughput, and user satisfaction are essential in this choice, as is the ability to scale and evolve with technological advancements.
We invite you to leverage our expertise to align your AI strategy with your business goals. Contact us to craft an AI solution that propels your business forward.
### Detailed overview of OpenAI ChatGPT
URL: https://www.mycustomai.io/blog/detailed-overview-of-openai-chatgpt
Summary: Discover ChatGPT's journey, highlighting GPT-3.5 and GPT-4's capabilities, including multimodal inputs, enhanced language understanding, and custom AI solutions.
### **0\. Introduction**
ChatGPT, developed by OpenAI, represents a significant advancement in artificial intelligence, offering versatile processing and generating text capabilities. The AI Agent developed by OpenAI is aptly named after the Generative Pre-trained Transformer technology, which it employs to generate responses to user prompts in a conversational dialogue. The AI Agent understands and responds to natural language queries through [Natural Language Processing](https://en.wikipedia.org/wiki/Natural_language_processing)(NLP).
### **1\. Description of ChatGPT**
ChatGPT represents OpenAI's iterative progress in artificial intelligence, evolving from a text-based model to one with multimodal capabilities. Its development signifies OpenAI's commitment to enhancing machine understanding and generation of human language, facilitating more nuanced and sophisticated interactions between humans and AI.
Each version of ChatGPT builds on the foundation of its predecessors, aiming to bridge the gap between AI communication abilities and the complexities of human dialogue.
#### **1.1 GPT-3.5**
- Utilizes deep learning algorithms to generate text that can mimic human writing styles.
- Capable of answering questions, composing essays, summarizing texts, and more, based on a vast database of language patterns.
- Marks a significant advancement in natural language processing, offering improved interaction quality over previous versions.
#### **1.2 GPT-4( Enhanced language model with ChatGPT Plus)**
- It introduces the ability to process text and image inputs, enhancing its understanding and output versatility.
- Achieves [human-level performance](https://cdn.openai.com/papers/gpt-4.pdf) on various professional and academic benchmarks, including a top 10% score on a simulated bar exam, underscoring its advanced understanding and generation capabilities.
- Incorporates a [post-training alignment process](https://cdn.openai.com/papers/gpt-4.pdf) to improve factuality and adherence to desired behaviors, reflecting AI safety and reliability advancements.
### **2\. Features**
ChatGPT's advancements through GPT-3.5 and GPT-4 have introduced various features enhancing AI's interaction capabilities. Each iteration brings specific enhancements, some available broadly and others exclusive to GPT-4.
- **Advanced Natural Language Understanding and Generation**: Available across GPT-3 and GPT-4, enabling complex text generation and understanding.
- **Multimodal Input Processing**: Exclusive to GPT-4, this feature allows the model to understand and generate responses based on text and image inputs.
- **Professional and Academic Benchmark Performance**: GPT-4 users benefit from human-level performance in various benchmarks, a testament to its advanced capabilities.
- **Safety and Alignment**: GPT-4 enhances safety and alignment, ensuring responses align with human values and factual accuracy.
- **Global Language Support**: GPT-3.5 and GPT-4 offer multi-language support, with GPT-4 expanding capabilities to include a broader range of languages.
- **Uploading and Generating Images in ChatGPT**: Exclusive to ChatGPT 4 users with Plus accounts, this feature allows for uploading images for discussion or problem-solving and generating new images directly within the ChatGPT interface, leveraging the capabilities of DALL-E 3
- **Custom GPT Creation and Sharing**: ChatGPT Plus allows users to create custom GPTs without writing code, tailoring them to specific needs such as recipe recommendations or technical support. These custom GPTs can be shared or monetized through a GPT Store.
### **3\. Pricing**
OpenAI offers two primary plans for accessing ChatGPT, catering to a range of users from casual enthusiasts to more demanding users who require the advanced capabilities of GPT-4.
Below is a brief overview of these plans:
#### **3.1 Free Plan**
Cost: $0 per month
- Access to GPT-3.5, enabling a wide range of conversational and text generation capabilities.
- Regular updates to the model ensure users benefit from continuous improvements.
- Accessibility across various platforms, including web, iOS, and Android, facilitates easy and flexible usage.
#### **3.2 Plus Plan**
Cost: $20 per month
- Exclusive access to GPT-4, OpenAI's most advanced model, offering enhanced conversational depth and understanding.
- Chatting with images and voice and creating images expands the interactive possibilities beyond text.
- Options to use and build custom GPTs, allowing for tailored experiences and functionalities.
- Includes all the features in the Free plan, ensuring a comprehensive set of tools and capabilities.
These plans provide scalable options for users, ranging from those seeking basic access to AI conversational tools to those requiring the full spectrum of capabilities offered by the latest advancements in AI technology.
### **4\. MyCustomAI**
OpenAI's GPT models are versatile and can handle various tasks in different domains. Although they can solve most cases, businesses may require customized and tailored solutions for specific and specialized use cases.
- Scalability and Data Requirements: The standard GPT models may not be optimal for businesses with extremely large proprietary datasets without significant customization. The sheer scale of data—billions of tokens—necessitates a tailored approach to ensure the AI model can effectively learn and provide valuable insights.
- Cost and Practicality: Developing custom AI solutions that modify every step of the model training process, from pre-training to post-training reinforcement learning, can be prohibitively expensive. This high cost makes it impractical for many organizations, especially smaller ones, to leverage OpenAI's GPT models for their specific needs.
MyCustomAI caters to enterprises aiming to leverage AI for niche applications or to process extensive, proprietary datasets. MyCustomAI provides customized and powerful AI services that transform businesses by addressing their unique challenges and opportunities.
- Tailored Solutions: MyCustomAI addresses the unique requirements of each enterprise, from niche applications to handling large-scale proprietary data.
- Broad Accessibility: This service extends the reach of AI's transformative power, offering specialized solutions that adapt to the diverse challenges and opportunities within various business sectors.
### **5\. Conclusion**
In conclusion, OpenAI's ChatGPT marks a significant milestone in the evolution of artificial intelligence, providing a comprehensive suite of tools that cater to a wide range of conversational and creative needs. From the foundational GPT-3.5 to the advanced GPT-4, OpenAI offers scalable solutions that bridge the gap between human complexity and AI's capabilities. Introducing features like multimodal input processing and custom GPT creation underscores OpenAI's commitment to innovation and accessibility in AI technologies.
### **6\. References**
1. (2023, March 27). _GPT-4 Technical Report_. Retrieved from [https://cdn.openai.com/papers/gpt-4.pdf](https://cdn.openai.com/papers/gpt-4.pdf)
2. OpenAI. (n.d.). _Custom models_. Retrieved from [https://openai.com/form/custom-models](https://openai.com/form/custom-models)
### Is ChatGPT Safe?
URL: https://www.mycustomai.io/blog/is-chatgpt-safe
Summary: overview of ChatGPT’s safety, discussing everything from misinformation risks to data security and privacy concerns.
### 0\. Introduction
In an era dominated by digital innovation, artificial intelligence (AI) has emerged as a cornerstone technology influencing numerous industries and daily interactions. Among these AI advancements, language models like ChatGPT have garnered significant attention for their ability to generate human-like text based on prompts provided by users. While these models offer immense potential for enhancing communication, it's imperative to understand their safety from multiple perspectives. This article aims to elucidate the safety considerations of using ChatGPT, focusing on its information reliability, operational security, and data handling practices.
### 1\. Is ChatGPT Safe from an Information Perspective? - Hallucination
#### 1.1 Description
ChatGPT, a state-of-the-art language model developed by OpenAI, operates by predicting text based on patterns and examples from a vast dataset. One limitation of this model is the phenomenon known as "hallucination," where the AI generates plausible but factually incorrect or misleading information. See this article for more details: [Biggest Strengths and Limitations of LLMs.](/blog/llms-top-strengths-and-worst-weaknesses)
#### 1.2 Risks
The risk of hallucination poses a significant challenge in scenarios requiring precise and factual information.
- For example, relying on ChatGPT for medical advice or detailed technical solutions can lead to inaccuracies that may have serious repercussions.
- Additionally, the model's training data has a cutoff date, meaning it does not possess information on developments occurring after its last update, further compounding the risk of outdated or incorrect data.
See this [article](https://www.sciencedirect.com/science/article/pii/S0925753523001868) for more details.
### 2\. Is ChatGPT Safe as a Tool? - Data Breaches
#### 2.1 Description
ChatGPT is implemented within a web application framework, which inherently involves storing and processing user data. This setup is similar to many modern web applications that handle personal and sensitive information.
#### 2.2 Risks
As with any web-based service, there is a potential risk of data breaches. These can occur through various means such as hacking, phishing, or even through business account takeovers. The consequences of such breaches can be severe, exposing user data and potentially leading to identity theft or other forms of cybercrime.
### 3\. Is ChatGPT Safe from Data Leakage?
#### 3.1 Description
ChatGPT learns by analyzing the patterns in the data it was trained on. When users interact with ChatGPT, they often input unique and sometimes sensitive information, which could potentially be used to train future versions of the model.
#### 3.2 Risks
- If sensitive data is not adequately protected, there is a risk that it could be inadvertently exposed during the model's retraining process. Moreover, techniques such as membership inference attacks can potentially be used to determine whether specific data was used in the training set, posing a risk of data leakage. See, for more details, this [article](https://arxiv.org/pdf/2402.07841.pdf).
- ChatGPT could leak information between users if it is put under pressure, see this [report](https://static1.squarespace.com/static/6593e7097565990e65c886fd/t/65a6f27438891c1e229bbbef/1705439862171/deception_under_pressure.pdf) for more details.
### 4\. Conclusion
The deployment of AI technologies like ChatGPT presents various safety challenges that must be navigated carefully. Users and developers alike should be aware of the potential information inaccuracies due to hallucinations, risks of data breaches, and the possibility of data leakage. By understanding and addressing these issues, we can better safeguard our interactions with AI systems, ensuring they are secure and reliable resources.
### 5\. References:
- [The risks of using ChatGPT to obtain common safety-related information and advice](https://chat.openai.com/c/f77b0e9f-1e6c-40b8-bac2-cd80b49f55b5#)
- [Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure](https://chat.openai.com/c/f77b0e9f-1e6c-40b8-bac2-cd80b49f55b5#)
- [Do Membership Inference Attacks Work on Large Language Models?](https://arxiv.org/pdf/2402.07841.pdf)
### AI: Knowledge Distillation Technique
URL: https://www.mycustomai.io/blog/knowledge-distillation
Summary: Knowledge Distillation: A technique enabling smaller models to achieve the performance of larger counterparts, enhancing AI efficiency and applicability.
### **0\. Introduction**
Knowledge Distillation is a refined technique in machine learning that facilitates knowledge transfer from a complex, larger model (teacher) to a simpler, more efficient one (student). This method enhances model training efficiency and ensures smaller models achieve similar accuracy and performance levels as their larger counterparts, making it a crucial strategy for optimizing AI applications in various sectors.
### **1\. Motivation**
The inception of knowledge distillation is primarily motivated by the intricate process of model creation, which traditionally involves four pivotal steps, starting with the generation of training data. This initial phase is notably the most challenging, requiring examples of input-output pairs to train a model effectively. The subsequent steps encompass model selection, training on the generated data, performance measurement, and optimization based on these metrics. Check this [link](/what-is-custom-ai) for more details.
###### **Training Data Generation Challenges**
- **Cost**: Human annotation of training data requires significant financial investment.
- **Time**: The process is time-consuming, involving extensive periods for hiring and training annotators.
- **Large** Dataset Requirement: Effective model training demands vast datasets, increasing complexity and resource needs.
### **2\. Knowledge Distillation Technique**
Knowledge distillation emerges as a strategic solution to these hurdles, offering a pathway to generate training data more efficiently, with reduced costs and time. Knowledge Distillation democratizes access to advanced computational intelligence and streamlines the deployment of sophisticated AI solutions.
- **Teacher Model**: A comprehensive, high-performing model is used as the knowledge source.
- **Student Model**: A more compact model that replicates the teacher's performance.
- **Loss/Error Function**: Utilized to quantify and minimize the discrepancy between the student's and teacher's outputs, ensuring practical knowledge transfer.
This [source](https://arxiv.org/pdf/1503.02531.pdf) provides a comprehensive explanation of distilling knowledge in a neural network.
### **3\. Advantages**
While the teacher model is adept at providing accurate predictions, the student model, through its innovative approach, has achieved comparable levels of accuracy with significantly fewer resources and faster computation time. This makes it an attractive option for those prioritizing efficiency in their operations.
#### **3.1 Cost and Time to Create a Model Goes Down:**
It substantially reduces the resources and time required for model development.
#### **3.2 Laser-focused Model on a Specific Use Case:**
Enables precise tailoring to specific applications, enhancing its effectiveness in targeted scenarios.
#### **3.3 Compactness and Speed**:
Due to its smaller size, the student model operates more swiftly and is more straightforward to manage, making it ideal for practical deployment scenarios.
### **4\. The Phi-1 Model Case Study**
A standout example of Knowledge Distillation's potential is observed in developing the Phi-1 language model, showcasing a practical application in AI.
- **Teacher Model**: Utilizes ChatGPT 3.5, a comprehensive language model renowned for its extensive dataset and coding proficiency.
- **Student Model**: A streamlined GPT model tailored for efficiency, operating with a reduced parameter count while maintaining commendable performance.
- **Achievements**: Despite its compact size, Phi-1 demonstrates exceptional capability, achieving over 50% accuracy in Python coding evaluations—a testament to the model's optimization through Knowledge Distillation.
Refer to the Microsoft Research publication on [Textbooks Are All You Need](https://www.microsoft.com/en-us/research/publication/textbooks-are-all-you-need/) for detailed insights.
### **5\. Beyond Text: Image Case Study**
Knowledge Distillation proves its versatility in text-based applications and across various AI domains, including vision. A prime example is [Meta AI's DINOv2](https://ai.meta.com/blog/dino-v2-computer-vision-self-supervised-learning/), a pioneering computer vision model utilizing self-supervised learning. DINOv2 showcases remarkable adaptability and performance, capable of learning from any collection of images without the need for labeled data. This approach broadens the applicability of Knowledge Distillation and sets a new standard in training AI models, emphasizing its potential to enhance state-of-the-art computer vision technologies.
### **6\. Conclusion**
Knowledge Distillation is a pivotal technique for optimizing AI model efficiency and specificity, effectively addressing computational efficiency and model performance challenges. It streamlines AI development and enables broader application across diverse machine learning domains by facilitating knowledge transfer from expansive teacher models to compact student models. The practical implementations, such as the Phi-1 and DINOv2 models, underscore the technique's significance, demonstrating its essential role in AI technologies' ongoing evolution and optimization.
### **7\. References**
1. Hinton, G., Vinyals, O., & Dean, J. (2015). _Distilling the Knowledge in a Neural Network_. Google Inc., Mountain View. Retrieved from [arXiv](https://arxiv.org/pdf/1503.02531.pdf).
2. Gunasekar, S., Zhang, Y., et al. (2023). _Textbooks Are All You Need_. Retrieved from [Microsoft Research](https://www.microsoft.com/en-us/research/publication/textbooks-are-all-you-need/).
3. Meta AI. (2023, April 17). _DINOv2: State-of-the-art computer vision models with self-supervised learning_. Retrieved from [Meta AI Blog](https://ai.meta.com/blog/dino-v2-computer-vision-self-supervised-learning/).
### Playbook: How to choose the best LLM for your own usecase?
URL: https://www.mycustomai.io/blog/playbook-how-to-choose-the-best-llm-for-your-own-usecase
Summary: A comprehensive guide to choosing the best Large Language Model (LLM) for your project, focusing on accuracy, cost-efficiency, reliability, and safeguarding privacy.
### 0\. Introduction
Selecting the appropriate Large Language Model (LLM) is pivotal for tasks requiring nuanced understanding and generation of text. This guide delineates essential criteria—accuracy, cost, reliability, and privacy—to assist in navigating through the complex ecosystem of LLMs. Each factor plays a critical role in determining the efficacy and applicability of an LLM to your specific needs, ensuring a well-informed decision that aligns with both technical requirements and strategic goals.
### 1\. Accuracy
#### 1.1 Definition
Accuracy in the context of Large Language Models (LLMs) refers to the model's performance on a specific task. This performance is often measured against pre-determined benchmarks to assess the LLM's capabilities in various domains.
#### 1.2 Benchmarks
When assessing the accuracy of Large Language Models (LLMs), it's crucial to consider their performance on specific tasks. Benchmarks play a vital role in this evaluation, providing standardized challenges that test different aspects of an LLM's capabilities. For instance, as reported by Anthropic, various models like Claude 3 and GPT-4 show distinct performance levels across tasks such as mathematics, language understanding, and common knowledge questions.
Platforms like the LMSYS Chatbot Arena, a crowdsourced open platform for LLM evaluations, enrich this landscape by ranking LLMs based on over 400,000 human preference votes using the Elo system. They offer insights into user preferences and model effectiveness in conversational contexts. Learn more about the LMSYS Chatbot Arena [here](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
A challenge with benchmarks is data contamination, where a model might have been trained on data that includes the test problems, leading to artificially high performance.
#### 1.3 Arena
'Arenas' inspired by competitive gaming, such as chess, have been developed where models like ChatGPT, Claude, and others are pitted against each other to rank their effectiveness.
The LMsys Chatbot Arena, for example, allows various models to compete, producing an ELO-like score that indicates their relative strengths. This method, while insightful, does not always clarify why one model outperforms another, emphasizing the need for a more nuanced approach to evaluating model performance.
For a detailed look into benchmarks and rankings, Anthropic's [Claude 3 family](https://www.anthropic.com/news/claude-3-family) and the LMsys Chatbot Arena [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) provide detailed insights into the current standings of various models.
Schaeffer's [paper](https://arxiv.org/pdf/2309.08632.pdf) highlights benchmarks and their potential drawbacks, such as the risk of data contamination. It proposes that pre-training on the test set is all one might need to achieve impressive results.
### 3\. Cost
The cost of Large Language Models (LLMs) varies significantly between open-source and commercial offerings. Open-source options like Mistral AI's models can be more affordable per-token basis, with pricing starting at $0.25 per million tokens.
#### 3.1 LLM Pricing Structures
##### 3.1.1 Open-Source Models
A cost analysis must weigh the initial setup investments against the operational costs. Open-source LLMs are generally less expensive per token basis but require more upfront customization and infrastructure development, which could lead to higher initial technical costs.
##### 3.1.2 Commercial LLM
Commercial models, while potentially higher in per-token cost, offer simplified integration. For example, ChatGPT has plans starting at $20 per month, offering additional tools and support. Google's Gemini, on the other hand, provides a pay-as-you-go tier starting at $0.000125 per 1K characters.
These models cater to different operational scales and integration needs, underscoring the importance of evaluating setup and long-term costs relative to specific project requirements and privacy considerations.
### 4\. Reliability
#### 4.1 Rate Limits
Reliability in the context of LLMs is often dictated by rate limits, which control the volume of interactions per time unit to prevent abuse and ensure fair access. Different tiers offer varying limits, affecting how many tokens per minute can be processed. OpenAI's rate limit system, detailed in its [Rate Limits Guide](https://platform.openai.com/docs/guides/rate-limits?context=tier-two), outlines these restrictions and provides strategies to manage them efficiently.
#### 4.2 Service Level Agreements (SLAs)
Unlike traditional software services, many LLM providers, including OpenAI, currently do not offer public SLAs to guarantee uptime or performance, which can be a concern for applications requiring high reliability.
OpenAI, however, is working towards publishing SLAs and offers a [Status Page](https://help.openai.com/en/articles/5008641-is-there-an-sla-for-latency-guarantees-on-the-various-engines) for real-time operational updates. This page helps users track the system's health and performance, which is critical for those with stringent latency needs.
In high-volume scenarios, companies need to consider these factors as generous rate limits may still fall short for extensive operations, and the absence of formal SLAs means there's no guaranteed model availability or consistent response times.
### 5\. Privacy Concerns with LLMs
Privacy is a significant concern when interacting with LLMs, as data fed into these models may be utilized to further their training. This process has the potential to expose sensitive information to unintended parties inadvertently.
In scenarios where users disclose private or proprietary information, there is a risk that competitors or external entities could access such data.
The privacy implications are considerable, necessitating careful consideration of the type of data shared with LLMs and the choice of models for tasks involving confidential information.
### 6\. Playbook: On How to evaluate
#### 6.1 Accuracy: Custom Benchmark Development
Creating domain-specific benchmark data is critical for accurately evaluating an LLM's performance. By annotating a tailored dataset with expected responses, you establish a clear metric for success that is directly aligned with your unique requirements. This approach ensures the LLM's effectiveness is measured against the nuances of your specific domain.
#### 6.2 Cost
##### 6.2.1 Estimating Excepted Volume
Begin by estimating the expected volume of usage for the LLM. This foundational step informs variable and fixed cost calculations, ensuring budgetary alignment with project needs.
##### 6.2.2 Variable Cost Analysis
Examine the variable costs associated with each provider, typically tied to the number of tokens processed or API calls made. This granular analysis allows for accurate operational budgeting.
##### 6.2.3 Fixed Cost Evaluation
Identify fixed costs, such as monthly or annual subscriptions and initial setup fees, to fully understand the financial commitment required for LLM integration.
#### 6.3 Reliability
##### 6.3.1 Multi-Provider Strategy
It's advisable not to rely solely on a single LLM provider for enhanced reliability. Using multiple providers ensures continuity in case one service becomes unavailable.
##### 6.3.2 Orchestration Frameworks Support
Frameworks like LangChain and LLamaIndex offer orchestration capabilities that seamlessly facilitate using multiple LLMs, allowing for an automatic fallback mechanism.
This approach to reliability maximizes uptime and service consistency across LLM applications.
#### 6.4 Privacy
For projects where privacy is important, leveraging open-source models is recommended. These models allow for in-house operation, minimizing the risk of confidential data exposure to external entities. Open-source solutions provide the flexibility to adapt security measures to your specific requirements, ensuring data privacy and protection.
### 7\. Conclusion
When selecting a Large Language Model (LLM) for your project, it's essential to consider factors such as accuracy, cost, reliability, and privacy. Understanding and evaluating these core aspects can ensure a strategic fit for their needs. Given the fast-paced advancements in LLM technology, a strategic, informed approach is crucial.
Custom AI leverages extensive experience in evaluating these criteria to guide you toward the most suitable LLM option for your specific requirements. Our approach ensures that your choice is technically sound and strategically aligned with your goals, providing a solid foundation for your AI-driven initiatives.
### 8\. References
1. Hugging Face. (n.d.). Open LLM Leaderboard. Hugging Face Spaces. Retrieved from [huggingface.co/spaces/HuggingFaceH4/open\_llm\_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
2. Anthropic. (2024, March 4). _Introducing the next generation of Claude_. Retrieved from [](https://www.anthropic.com/news/claude-3-family)[anthropic.com/news/claude-3-family](http://anthropic.com/news/claude-3-family).
3. Schaeffer, R. (2023, September 19). _Pretraining on the Test Set Is All You Need._ Retrieved from [](https://arxiv.org/pdf/2309.08632.pdf)[arxiv.org/pdf/2309.08632.pdf](http://arxiv.org/pdf/2309.08632.pdf).
4. LMsys. (2024, March 13). _Chatbot Arena Leaderboard. Hugging Face Spaces_. Retrieved from [chatbot-arena-leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
5. OpenAI. (n.d.). _Rate Limits_. OpenAI Documentation. Retrieved from [platform.openai.](https://platform.openai.com/docs/guides/rate-limits?context=tier-two)
6. OpenAI. (n.d.). _Is there an SLA for latency guarantees on the various engines?_ OpenAI Help Center. [Accessed](https://help.openai.com/en/articles/5008641-is-there-an-sla-for-latency-guarantees-on-the-various-engines).
### Top 6 Approaches to Overcome the LLMs Limitations
URL: https://www.mycustomai.io/blog/top-6-approaches-to-overcome-the-llms-weaknesses
Summary: Top 6 Approaches to Overcome the LLMs Weaknesses and Limitations and to increase the LLMs strength
### **0\. Introduction**
Large Language Models (LLMs) like ChatGPT have revolutionized various industries with their advanced capabilities in processing and generating human-like text. However, it's crucial to recognize their inherent limitations.
While proficient in tasks like summarization and creative draft generation, these models can struggle with issues like generating reliable text (often leading to hallucinations), limited reasoning abilities, especially in complex mathematical contexts, and vulnerabilities to security threats.
To learn more about these limitations, refer to our [detailed blog](/blog/llms-top-strengths-and-worst-weaknesses).
This article focuses on the top six approaches to effectively overcome these challenges and ensure LLMs are utilized to their fullest potential responsibly and efficiently.
Prompting
Functions
Retrieval-augmented generation
Knowledge Distillation
Fine Tuning
Training from Scratch
Easy
Complex
_Top 6 Approaches to Overcome the LLMs Weaknesses_
### **1\. Approach 1: Prompting**
#### **1.1 Definition**
Prompt engineering involves framing queries for language models in a way that guides them to produce the most relevant and accurate responses. It's a strategic process ensuring clarity, context, and precision in the questions.
#### **1.2 Example of Illustration**
Consider a query about summarizing a scientific article. A basic prompt like "Summarize this article" can be vague. An engineered prompt would be more specific: "Summarize this article in three key points focusing on its methodology, findings, and implications."
#### **1.3 Link to Read More**
For a comprehensive guide on prompt engineering and strategies to test changes systematically, visit our detailed article about: [What are Different Prompt Strategies?](/blog/what-are-different-prompt-strategies)
### **2\. Approach 2: Functions**
#### **2.1 Definition**
Functions are external capabilities that LLMs can leverage to perform tasks beyond their inherent abilities, such as data retrieval or executing complex operations.
#### **2.2 Example of Illustration**
An example is using the DALL-E function to create images. When GPT-4 is prompted to generate a picture, it cannot natively output this picture. It calls upon DALL-E to produce the visual content based on descriptive text input.
#### **2.3 Link to Read More**
For a detailed understanding of how LLMs can use functions, see MemGPT's research at [MemGPT Research](https://research.memgpt.ai/), [Toolformer](https://arxiv.org/pdf/2302.04761.pdf) by Meta, and the guide on [Function Calling with LLMs](https://platform.openai.com/docs/guides/function-calling/introduction).
### **3\. Approach 3: Retrieval-augmented Generation**
#### **3.1 Definition**
Retrieval-augmented generation (RAG) is a technique that combines the generative power of LLMs with external databases to enhance the model's responses with up-to-date and detailed information.
#### **3.2 Example of Illustration**
For instance, when an LLM is asked about the latest advancements in renewable energy, RAG would enable it to first pull the most recent research data from a scientific database, ensuring that the response not only reflects the LLM's base knowledge but also includes the latest findings in the field.
Consider another scenario - an LLM tasked with providing financial advice. Using RAG, it queries the latest stock market data and expert analysis from a financial database before responding, thus offering current and reliable investment insights beyond its initial training data, which does not contain the latest market news and trends.
#### **3.3 Links to Read More**
For an in-depth exploration of RAG, visit our detailed article about: [What is Retrieval-Augmented Generation (RAG)?](/blog/what-is-retrieval-augmented-generation-rag)
### **4\. Approach 4: Knowledge Distillation**
#### **4.1 Definition**
Knowledge Distillation is a technique for streamlining complex models. It enables smaller models to emulate the performance of larger ones by training on their outputs and optimizing computational resources without significantly compromising accuracy.
#### **4.2 Example of Illustration**
A practical application of Knowledge Distillation is evident in developing the phi-1 language model. Phi-1, a specialized Transformer model for Python coding, leverages 1.3 billion parameters and is fine-tuned with diverse coding data, including actual Python code, StackOverflow Q&As, and synthetic exercises generated by GPT-3.5.
Despite its smaller size, phi-1 harnesses high-quality data and synthetic exercises from a more extensive model to achieve remarkable accuracy in coding benchmarks (achieves over 50% accuracy on Python coding evaluations,) showcasing the effectiveness of Knowledge Distillation and surpasses (5x) larger models.
#### **4.3 Links to Read More:**
For an in-depth exploration of Knowledge Distillation, please check our in-depth article on [Knowledge Distillation](/blog/knowledge-distillation).
### **5\. Approach 5: Fine Tuning**
#### **5.1 Definition**
Fine tuning is a process where a pre-trained model is adapted to a specific task by continuing the training phase and adjusting the model weights based on a particular dataset. This technique leverages the knowledge the model has already gained during its initial broad training and focuses it on the nuances of a targeted application.
#### **5.2 Example of Illustration**
Consider a language model trained on general English text. To fine-tune it for legal document analysis, it would be further trained on a dataset of legal documents. This specialized training adjusts the model’s weights to understand better and generate text in legal contexts, improving its performance on tasks like contract analysis or case prediction.
#### **5.3 Links to Read More**
The following [paper](https://arxiv.org/pdf/2002.06305.pdf) provides extensive research and findings to explore fine-tuning methods and their impact on model performance.
### **6\. Approach 6: Training from Scratch**
#### **6.1 Definition**
Training a Large Language Model (LLM) from scratch involves building a model entirely without using pre-trained components. This process includes selecting a model architecture, curating a diverse and extensive dataset, and conducting the training process. This approach is often resource-intensive and complex, requiring significant computational power and data-handling expertise.
#### **6.2 Example of Illustration**
Bloomberg's approach to creating a domain-specific LLM for financial technology is a notable example. They developed "BloombergGPT," a 50-billion-parameter model. The training involved a meticulously curated dataset comprising financial documents, news, filings, and general-purpose datasets. Despite large-scale training and data curation challenges, BloombergGPT excelled in financial domain tasks, demonstrating the potential of training from scratch for specialized applications.
#### **6.3 Link to Read More**
The detailed study and methodology can be explored in the research paper "Training Large Language Models: A Deep Dive into BloombergGPT," which is [available here](https://arxiv.org/pdf/2303.17564.pdf).
### **7\. High-level relative complexity**
In developing the Large Language Model (LLM), the complexity of approaches ranges widely. At one end of the spectrum, prompting is the most accessible technique, offering a user-friendly gateway to harnessing LLMs. As we move towards the other end, each successive method — from leveraging functions and retrieval-augmented generation to sophisticated strategies like knowledge distillation and fine-tuning — demands incrementally greater technical expertise and computational resources. The most complex of these methods is training a model from scratch, requiring substantial data, infrastructure, and domain knowledge.
Moreover, the easier the approach used the less chance of closing the limitations that a LLM is facing. For instance, prompting an LLM that was never trained in financial data would undoubtedly lead to unreliable results and hallucinations. In other words, the specific needed approach would depend on the degree of customization targeted by the end user.
Prompting
Functions
Retrieval-augmented generation
Knowledge Distillation
Fine Tuning
Training from Scratch
Easy
Complex
_Spectrum of Complexity in LLM Development Approaches_
### **8\. Conclusion**
In conclusion, we've outlined the top six approaches to mitigate the limitations of Large Language Models, ranging from the relatively straightforward prompting to the more intricate process of training from scratch. Each method offers unique advantages and complexities tailored to enhance LLMs' performance in various scenarios.
As we explore each approach in our upcoming series of blogs, we aim to provide comprehensive insights, practical examples, and advanced strategies for fully leveraging the capabilities of LLMs across different industries and applications.
### **9\. References**
1. OpenAI. (2023). _Prompt Engineering Guide_. Retrieved from [OpenAI Documentation](https://platform.openai.com/docs/guides/prompt-engineering).
2. Packer, C., Fang, V., et al. (2023). _Towards LLMs as Operating Systems_. UC Berkeley. Retrieved from [MemGPT Research](https://research.memgpt.ai/)
3. Lewis, P., Perez, E., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In _NeurIPS 2020_. Retrieved from [NeurIPS Proceedings](https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf).
4. Hinton, G., Vinyals, O., & Dean, J. (2015). _Distilling the Knowledge in a Neural Network_. Retrieved from [arXiv](https://arxiv.org/abs/1503.02531).
5. Gunasekar, S., Zhang, Y., et al. (2023). _Textbooks Are All You Need_. Retrieved from [Microsoft Research](https://www.microsoft.com/en-us/research/publication/textbooks-are-all-you-need/).
6. Dodge, J., Ilharco, G., et al. (2020). Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping. Retrieved from [arXiv](https://arxiv.org/pdf/2002.06305.pdf).
7. Wu, S., Irsoy, O., et al. (2023). BloombergGPT: A Large Language Model for Finance. Retrieved from [arXiv](https://arxiv.org/pdf/2303.17564.pdf).
8. Bloomberg Professional Services. (2023). _Introducing BloombergGPT, Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance_. Retrieved from [Bloomberg](https://www.bloomberg.com/company/press/bloomberggpt-50-billion-parameter-llm-tuned-finance/).
### What are Different Prompt Strategies?
URL: https://www.mycustomai.io/blog/what-are-different-prompt-strategies
Summary: Unpacking LLM prompt strategies: From direct to custom fine-tuning.
## **1\. Introduction**
In artificial intelligence, prompt strategies in Large Language Models (LLMs) like GPT-4 have emerged as a pivotal factor in harnessing their full potential. This discussion is narrowly tailored to examine the specifics of prompt strategies utilized in LLMs. This includes examining direct, chain-of-thought, zero-shot, and few-shot learning prompts and fine-tuning with custom prompts.
These strategies uniquely influence how LLMs interpret and respond to human input, influencing the model's performance across various tasks and scenarios.
### **2\. Prompts in Large Language Models**
#### **2.1 Definition and Significance**
In Large Language Models (LLMs) like GPT-4, prompts serve as the crucial interface between human users and the model's capabilities. A prompt is an input sequence of text that guides the model to generate a specific output type. Its significance lies in its ability to effectively 'steer' the model towards desired functions or responses, whether generating text, translating languages, answering questions, or even performing complex reasoning tasks.
#### **2.2 Basic Mechanism**
Prompts are a form of instruction or query that sets the context for the LLM's response. The model, trained on vast amounts of text data, uses the prompt to access its learned patterns and information.
For instance, when provided with a prompt, the LLM generates a continuation by predicting the most likely subsequent sequence of words based on its training. This mechanism allows various applications, from generating creative content to solving analytical problems.
For more details, check out [recent studies](https://ar5iv.labs.arxiv.org/html/2102.07350) highlighting the role of prompts.
### **3\. Direct Prompting Strategy**
#### **3.1 Description**
Direct prompting in Large Language Models (LLMs) involves using clear, straightforward questions or commands to guide the model's response. The model responds based on its pre-existing knowledge and training, making this approach particularly effective for jobs where the model's response format is well-defined or predictable.
#### **3.2 Example**
A classic example of direct prompting is in translation tasks. For instance, the prompt could be "Translate from French to English: 'Bonjour, comment ça va?'" In this scenario, the model receives a command ("Translate from French to English") followed by the content that needs to be translated. The model then processes this prompt and generates a response based on its training in language translation, ideally producing the output: "Hello, how are you?"
### **4\. Chain-of-Thought Prompting**
#### **4.1 Description**
Chain-of-thought prompting guides a language model through an articulated sequence of reasoning. It's beneficial for tasks that require logical deduction, arithmetic computation, or intricate decision-making.
The model is effectively prompted to "think aloud," laying out its reasoning in a stepwise fashion that users can examine and follow. The following [resource](https://papers.nips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html) provides a comprehensive insight into the Chain-of-Thought prompting technique.
#### **4.2 Example**
A problem like "If a farmer has 15 apples and gives away all but 3, how many does he have left?" requires logical deduction.
A chain-of-thought prompt might be: "Consider how many apples the farmer starts with. Subtract the number he gives away to find out how many he has left. Show each step of your calculation."
The model would respond with: "The farmer starts with 15 apples. He gives away 15 - 3 = 12 apples. Therefore, he has 3 apples left."
This provides the answer and the logical progression to reach it, mimicking human problem-solving.
### **5\. Zero-Shot and Few-Shot Learning Prompts**
#### **5.1 Description**
Zero-shot learning prompts ask the model to perform tasks it has not been explicitly trained for, leveraging its generalized understanding. Without examples, the model must infer the task's requirements from the prompt alone.
#### **5.2 Example**
In a text sentiment classification task, a zero-shot prompt might be: "Determine the sentiment of the following review: 'I absolutely loved the friendly staff and the cozy atmosphere!'" The model uses its pre-trained knowledge to infer sentiment directly.
### **6\. Few-Shot Learning Prompts**
#### **6.1 Description**
Conversely, few-shot learning prompts supply the model with a small set of examples to 'prime' it for the task. These examples give the model a clearer understanding of the task's nature and the expected response format.
#### **6.2 Example**
For few-shot learning, the prompt would be preceded by examples
"\[Positive\] 'What a fantastic experience!' \[Negative\] 'It was a disappointing meal.' Now, determine the sentiment of the following review: 'I absolutely loved the friendly staff and the cozy atmosphere!'"
By providing positive and negative instances, the model has a reference for what kind of sentiment to associate with specific language cues in the task it is presented with.
The following [paper](https://www.researchgate.net/publication/375074387_Unleashing_the_potential_of_prompt_engineering_in_Large_Language_Models_a_comprehensive_review) discusses one-shot and few-shot prompting, highlighting zero-shot prompts.
### **7\. Fine-Tuning with Custom Prompts**
#### **7.1 Description**
Fine-tuning with custom prompts refines a language model's responses for particular domains or specialized tasks. This involves adjusting the model's parameters or training it on a tailored dataset, making it more proficient in generating context-specific or industry-related content.
#### **7.2 Example**
For composing a blog post on the 2024 U.S. Presidential Election, a fine-tuned model might receive a prompt like:
"Write a comprehensive analysis of the key policies proposed by the candidates in the 2024 U.S. Presidential Election, emphasizing their potential impact on healthcare and foreign policy."
This custom prompt, crafted post-fine-tuning, steers the model to synthesize information within its training relevant to U.S. politics, policy specifics, and the electoral context, generating a topic-specific article.
### **8\. When Prompting Works Well and When It Does Not**
#### **8.1 Effective Use of Prompting**
Prompting in LLMs is most effective when the task aligns with the model's pre-trained knowledge and data. For example, direct prompts excel in structured tasks like language translation or factual queries, where the expected response type is clear.
Chain-of-thought prompting shines in logical or mathematical problem-solving, leveraging the model's ability to articulate stepwise reasoning.
#### **8.2 Limitations of Prompting**
Prompting is less effective in tasks requiring understanding beyond the model's training, such as highly specialized knowledge areas or recent events not covered in the training data. In these cases, fine-tuning with custom prompts and tailoring the model to specific domains or current topics can be more beneficial.
#### **8.3 Choosing the Right Strategy**
The prompting strategy depends on the task's complexity and the model's training. Zero-shot and few-shot learning are useful for tasks that require a degree of generalization or contextual interpretation, while chain-of-thought prompting is ideal for detailed problem-solving. Fine-tuning is optimal for niche or highly specialized tasks.
### **9\. Conclusion**
Prompt strategies in Large Language Models (LLMs) like GPT-4 are critical for optimizing task-specific performance. The effectiveness of each prompt strategy depends heavily on the model's training scope and the nature of the task at hand. If the prompt strategy does not align with
Selecting the right prompt strategy is critical, and one must carefully evaluate the model's training data and the task's objectives to determine the most effective prompt strategy. By doing so, they can optimize the performance of the LLM and produce accurate and relevant output.
### **10\. References**
1. Reynolds, L., McDonell, K. (2022). _Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm_. Retrieved from [ar5iv](https://ar5iv.labs.arxiv.org/html/2102.07350).
2. Wei, J., et al. (2022). _Chain-of-Thought Prompting Elicits Reasoning in Large Language Models_. Retrieved from [NeurIPS Proceedings](https://papers.nips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html).
3. Chen, B., Zhang, Z., Langrené, N., et al. (2023). _Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review_. Retrieved from [ResearchGate](https://www.researchgate.net/publication/375074387_Unleashing_the_potential_of_prompt_engineering_in_Large_Language_Models_a_comprehensive_review)
### What are the Mechanics inside LLM?
URL: https://www.mycustomai.io/blog/what-are-the-mechanics-inside-llm
Summary: Exploring the Inner Workings of LLMs: Uncover how Large Language Models like ChatGPT use statistical analysis and extensive data to predict language patterns and facilitate in-context learning.
## **1\. Introduction**
Large Language Models (LLMs) like ChatGPT represent a sophisticated blend of technology and language skills, mainly through their ability to predict the next word in a sentence. This skill is based on more than just algorithms; it's also about understanding statistics deeply. Here, we look into how these models use extensive text data to make informed and accurate language choices, ascertaining a refined and precise way of handling language.
### **2\. The Concept of Next Word Prediction**
Next Word Prediction is a process central to Large Language Models (LLMs) like ChatGPT. It involves predicting the most likely subsequent word in a text sequence. This process is not about random guessing; it's a calculated estimation based on patterns learned from extensive text data.
In this predictive task, the model uses a probabilistic approach. It evaluates multiple word options and their likelihood of occurrence following the given text. This method is grounded in statistical analysis, where the model has been trained on vast datasets to recognize and replicate common linguistic patterns.
### **3\. Illustration**
#### **3.1 Similarity to Human Language Understanding**
In everyday conversations, humans often predict what the other person will say. For instance, in the phrase "I like to eat...", we instinctively know that the next word will likely be food-related. It's highly unlikely someone would say, "I like to eat car," because it doesn't make sense. This ability is based on our understanding of language patterns and context.
#### **3.2 Difference from Human Prediction**
##### **3.2.1 Role-Play Analogy:**
How Large Language Models (LLMs) like ChatGPT predict words fundamentally differs from human prediction. The concept of role-play can be a helpful analogy for understanding dialogue agents.
Humans use various psychological concepts – beliefs, desires, goals – to predict and interpret speech. But when an LLM 'role-plays,' it doesn't commit to a single character or narrative. Instead, it's like an actor in improvisational theatre, capable of adopting any role from an infinite range of possibilities.
##### **3.2.2 Maintaining Multiple Narratives:**
As a conversation with an LLM progresses, it doesn't adhere to a single character or storyline. Instead, it maintains a multitude of potential narratives, constantly adjusting based on the dialogue's context. Each word it predicts is like choosing a path in a branching tree, each branch representing a different narrative possibility.
##### **3.2.3 Navigating the Tree of Possibilities:**
This process is stochastic and non-deterministic, meaning it's inherently unpredictable and varies each time. Imagine a dialogue with an LLM as a tree of possibilities to visualize this. Each branch represents a different direction the conversation could take, with the LLM navigating this tree in real-time, choosing the most likely or relevant path based on the current context.
This approach sets LLMs apart from human conversation, where our predictions are more deterministic, based on personal experiences, and a more fixed understanding of the world. Read more about it [here](https://www.nature.com/articles/s41586-023-06647-8).
### **4\. Implication**
#### **4.1 In-context learning**
In-context learning is a transformative concept that allows models to rapidly adapt to new tasks or recognize patterns based on examples provided in their training data. It's akin to giving the model a 'hint' of what is expected through examples, and it then generalizes this to new, unseen situations.
_1.1._ [In-context Learning in Large Language Models](https://arxiv.org/pdf/2005.14165.pdf)
Application in LLMs: For instance, consider cleaning up text data. If we show LLM examples where random symbols are removed from words, the model learns to perform this task with new words it encounters, using only a few examples as guidance. This is demonstrated in Figure 1.1 from the source paper (source: [Language Models](https://arxiv.org/pdf/2005.14165.pdf).)
- **Learning Different Tasks**: The figure demonstrates learning through solving simple arithmetic, correcting misspelled words, and translating phrases from English to French.
- **Using Context for Predictions**: It utilizes the context from initial examples to accurately respond to new prompts, applying learned patterns to future interactions.
- **Potency in Larger Models**: Larger models excel in in-context learning, absorbing a wider range of skills and patterns.
- **Improved Learning from Contextual Clues**: These models are better at learning from contextual clues, which is crucial for managing diverse tasks.
- **Mini-Learning Sessions**: Each new prompt triggers a mini-learning session, where the model adjusts its parameters for enhanced prediction and task completion.
_1.2._ [Efficiency of In-context Learning Across Model Sizes](https://arxiv.org/pdf/2005.14165.pdf)
This capability is groundbreaking because it allows LLMs to perform various tasks without extensive retraining or fine-tuning. Users can guide the model towards the desired outcome by simply providing examples within the input, making the interaction with the model intuitive and efficient.
Figure 1.2 from the [source paper](https://arxiv.org/pdf/2005.14165.pdf) visually encapsulates this idea with three sequences where the model demonstrates in-context learning. This approach marks a significant departure from traditional programming, offering a glimpse into how future AI systems will learn and adapt more to human learning.
#### **4.2 Chain of Thought**
The 'Chain of Thought' approach significantly advances how Large Language Models (LLMs) like ChatGPT process and respond to prompts, especially when solving complex problems. This method involves the model articulating its reasoning process, step by step, before providing an answer. It's akin to showing one's work in a math problem, which clarifies the thought process and increases the likelihood of reaching the correct conclusion.
##### **4.2.1 Improving Problem Solving:**
For example, when presented with a question that requires multiple steps to solve, standard prompting might lead to an incorrect answer because the model aims to conclude directly. However, with Chain of Thought prompting, the model is encouraged to break down the problem into intermediate steps, much like how a human would naturally reason through a problem. By doing so, the model generates a narrative of its reasoning, which leads to a correct final answer.
_1.3._ [Language Models Perform Reasoning via Chain of Thought](https://blog.research.google/2022/05/language-models-perform-reasoning-via.html)
#### **4.2.2 Enhancing Model Interpretability:**
The attached image illustrates this concept clearly. In the left column, you see examples of standard prompting, where the model outputs an incorrect answer. In the right column, Chain of Thought prompting is used, and the model provides the correct answer and explains the reasoning behind it. By including its 'thoughts,' the model effectively increases the 'signal,' or clarity, of its reasoning process before arriving at an answer.
This Chain of Thought process represents a shift towards more interpretable AI decision-making. It allows for improved response accuracy and an easier evaluation of how the model understands and approaches a given task.
### **5\. Conclusion**
The mechanics of Large Language Models (LLMs) such as ChatGPT are intricate and continually unfolding. While we have explored concepts like next word prediction, in-context learning, and the Chain of Thought, the full extent of these models' capabilities and the precise ways they operate are still subjects of ongoing research. The complexities of these AI systems are such that what might appear as emergent abilities could, upon closer examination, reveal layers of complexity that challenge our initial understandings.
For a deeper look into the current state of research, a recent paper (available at [arXiv:2304.15004](https://arxiv.org/pdf/2304.15004.pdf)) provides a critical analysis of these perceived emergent abilities. This publication is a valuable resource for anyone looking to grasp the nuances of LLMs and is one of the noteworthy contributions to the field.
### **6\. References**
1. Shanahan, M., McDonell, K., & Reynolds, L. (2023). _Role play with large language models_. Retrieved from [https://www.nature.com/articles/s41586-023-06647-8](https://www.nature.com/articles/s41586-023-06647-8).
2. Brown, T. B., Mann, B., Ryder, N., et al. (2020). _Language Models are Few-Shot Learners_. Retrieved from [arXiv:2005.14165v2](https://arxiv.org/pdf/2005.14165.pdf).
3. Wei, J., & Zhou, D. (2022, May 11). _Language Models Perform Reasoning via Chain of Thought._ Posted by Research Scientists, Google Research, Brain Team. Retrieved from [https://blog.research.google/2022/05/language-models-perform-reasoning-via.html](https://blog.research.google/2022/05/language-models-perform-reasoning-via.html).
4. Schaeffer, R., Miranda, B., & Koyejo, S. (2023). _Are Emergent Abilities of Large Language Models a Mirage?"_ Computer Science, Stanford University. Retrieved from [arXiv:2304.15004v2](https://arxiv.org/pdf/2304.15004.pdf).