AI
insights
Practical notes on AI, newest first.
Validating AI Tool Calls Before They Reach Backend Systems
A practical, layered approach to validating AI-generated tool calls before they can affect enterprise systems.
Designing Safe Text-to-SQL for Enterprise Databases
Enterprise Text-to-SQL requires strict authorization, a governed semantic layer, deterministic validation, isolated execution, and auditable results.
Managing Context Budgets for Long Document Q and A
Reliable long-document Q and A depends on allocating context across chunking, summaries, retrieval, prompts, evidence, and the final response.
Regression Testing Before Updating Production AI Models
A practical framework for testing how model upgrades affect answer quality, integrations, cost, safety, and established production workflows.
Using Semantic Caching to Reduce AI Latency and Cost
A practical guide to cache boundaries, similarity thresholds, invalidation, and safe rollout for enterprise AI systems.
Combining Vision, Text, and Human Review for AI Inspection
A practical architecture for combining visual evidence, written specifications, operational data, and human review in AI inspection.
Designing OCR and LLM Pipelines for Complex Documents
A practical architecture for preserving document layout, extracting reliable fields, and routing uncertain results safely.
Building an Enterprise LLM Gateway for Quotas and Cost Control
A practical architecture for governing LLM access, quotas, cost attribution, and safe switching between model providers.
Verifying RAG Citations Against Source Documents
A practical engineering approach to document provenance, claim-level validation, conflict handling, and auditable RAG citations.
A Practical Debugging Workflow for Inconsistent Enterprise AI Answers
A systematic workflow for isolating answer-quality problems across prompts, retrieval, models, tools, permissions, and enterprise integrations.
From Chatbot to Workflow Agent: When to Upgrade
Before letting enterprise AI take action, evaluate the workflow, system boundaries, operational risk, and real cost of integration.
Building and Maintaining Evaluation Sets for RAG Systems
A practical approach to sampling cases, labeling evidence, measuring each RAG layer, and governing evaluation sets as systems evolve.
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