Topic
AI Engineering
Agents, RAG and LLM features that survive production: contracts, evaluation, authority, cost and recovery.
What Does a Correct RAG Answer Cost? Count Retries, Review and Setup
Per-call price ranks RAG pipelines wrongly. Divide all work by accepted tasks, keep missing usage unknown, and see how volume and question mix change the winner.
An Agent Tool Is an API: Caller Authority, Receipts and Lost Replies
Treat agent tool calls like public API requests: take identity from the host, scope tools per request, store receipts with the effect and replay lost replies.
Your AI Chat Cannot Carry the Team's Architecture
A clean invoice rename breaks a monthly export because the reason lived in an old chat. Record the decisions agents would erase, make them executable, and put them where work starts.
RAG in .NET: retrieve the policy the caller can actually use
Build the retrieval side of RAG in C#: eligibility before top-k, grants and revisions in the query, chunks that keep their exceptions, and atomic revision publishing.
Buying an AI Product: What the Demo Quietly Gives You for Free
Two AI products write equally convincing release notes. Price the accepted result instead of the generation, find the one assumption that flips the decision, and pilot exactly that.
Who Owns AI-Written Code When Everyone Reviewed Something Else?
A three-line cleanup patch deletes pinned files after four green checks. Follow the hand-offs, find the one test that stops it, and give ownership real information and control.