AI
insights
Filtered field notes for AI work, newest first.
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.
Multi-Tenant AI Assistants: Practical Isolation and Context Management
Safe multi-tenant assistants enforce tenant boundaries across retrieval, memory, tools, caches, background jobs, and observability.
Designing Permissions for Enterprise AI Assistants: RBAC, Masking, and Query Scope
A practical architecture for enforcing identity, RBAC, masking, and query scope before enterprise data reaches an AI model.
Controlling AI Hallucination in Enterprise Scenarios
A practical engineering guide to reducing AI hallucination with knowledge boundaries, retrieval design, workflow controls, and governance.
Structured Output and Function Calling in Practice: Turning AI Responses into System Workflows
Practical guidance on schemas, tool boundaries, validation, and failure handling for production AI integrations.
Designing Multi-Model Routing and Fallback
Multi-model architecture is not just wiring more APIs together; it is a control system for quality, cost, latency, and availability.
Keeping a Knowledge Base from Going Stale
A practical maintenance model for keeping enterprise knowledge bases reliable enough for AI assistants and RAG systems.
Handing Off from AI to a Human Agent, Safely and Smoothly
A smooth AI-to-human handoff depends on clear escalation rules, safe context sharing, and operational systems that agents can actually use.
Managing and Versioning Prompts in Production
Prompts need the same operational discipline as code when AI features must survive model changes, audits, and real user traffic.
The Cost and Trade-Offs of Self-Hosting an LLM
Self-hosting an LLM is not just a GPU decision; it changes cost, operations, governance, and integration responsibilities.
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