Shipping RAG that survives production
Retrieval looks easy in a notebook and breaks under real traffic. Notes on evaluation, guardrails, and graceful failure.
A retrieval pipeline that scores well on a handful of test questions can still fall apart the moment real users hit it. The gap between a demo and a product is almost never the model. It is everything around it.
Evaluate before you trust
Build an evaluation set from real questions, not the ones that happen to work. Track retrieval quality and answer quality separately, because a good answer over the wrong context is luck, not a system.
Fail on purpose
Decide what happens when retrieval returns nothing useful. A confident wrong answer is worse than an honest 'I could not find that'. Guardrails and a clear fallback path are what make the thing safe to ship.
Written by Ansh Roshan
Get in touch