RAG that survives production

A RAG prototype is easy. A RAG system that stays accurate, fast and affordable under real traffic is not. The gap is mostly evaluation and guardrails.

Evaluate before you trust

Without a test set of real questions and graded answers, you are tuning blind. Evaluation is what turns “looks right” into “is right”.

Ground, then constrain

Retrieval grounds the model in your data; guardrails keep it from confidently inventing the rest. You need both.

Watch cost and latency like a hawk

Token cost and tail latency are product features. Budget them early or they will surprise you at scale.