How we built a document Q&A system with NestJS, PostgreSQL, pgvector, Hybrid Search (RRF), Prompt Caching, and Claude on Amazon Bedrock.

Building a demo chatbot is easy.

Building one that consistently answers correctly after weeks of document updates, changing requirements, and real user traffic is where things become interesting.

Most Retrieval-Augmented Generation (RAG) failures don't look like failures.

The API returns 200.

The model responds.

Infrastructure dashboards stay green.

Yet answer quality gradually gets worse.

You ask about error code E-4021 and receive documentation for E-4012 .

A recently updated document never appears in search results.

Prompt caching is enabled, but costs don't go down.

Hybrid search is implemented, but retrieval quality barely improves.

Nothing crashes.

Nothing raises an alert.

The chatbot simply becomes less useful over time.

For teams involved in application software development , the real work begins after the first successful response.…