AI
insights
Practical notes on AI, newest first.
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.
Landing AI Agents in Enterprise Workflows, and Their Limits
AI agents can improve enterprise workflows when their scope, permissions, integrations, and review paths are engineered deliberately.
A Practical Architecture for AI Customer Service on LINE OA
A field-tested architecture view for connecting LINE OA to AI support, covering webhooks, RAG, integrations, handoff, and operations.
Choosing a Vector Database: pgvector, Qdrant or Others
A practical engineering guide to choosing between pgvector, Qdrant, search engines and managed vector databases for enterprise RAG systems.
Five common pitfalls when adopting RAG in the enterprise
Enterprise RAG succeeds when knowledge governance, retrieval design, permissions, evaluation, and operations are treated as core engineering work.
RAG vs fine-tuning: which should companies choose?
Comparing RAG and fine-tuning across cost, maintenance, data security and update frequency — and the order most companies should choose in practice.
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