The Unglamorous Work Behind Trustworthy RAG

If your AI assistant is confidently making things up, the problem usually isn’t the model — it’s what you’re retrieving and how you’re grounding its answers.

Good RAG isn’t “throw the docs in a vector database.” It’s chunking strategy tested against real queries, retrieval precision measured on a labeled set, and source attribution shown to the user for every factual claim — so when it’s wrong, you (and your user) can tell.

This is the unglamorous 80% of building a trustworthy AI assistant. It’s also the part most vendors skip.