"RAG in production: where it actually breaks"
The first confidently wrong answer from a production RAG system rarely comes from the model. It comes from the pipeline around it. Four failure modes account for most of the damage — stale indexes, retrieval misses, chunk boundaries, and permission leaks — and none of them throw an exception.
The first confidently wrong answer from a production RAG system rarely comes from the model. It comes from the pipeline around it — the index that fell behind, the chunk that got cut mid-sentence, the retrieval step that fetched the wrong document entirely. If your users are reporting answers that are fluent, plausible, and wrong, the generation step is probably doing exactly what it was told, with the wrong material.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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