AI
insights
Filtered field notes for AI work, newest first.
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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