Shipping a RAG assistant that actually works in production

Why retrieval quality, evaluation sets and guardrails matter more than the model you pick.

Most RAG demos fall apart in production because the retrieval layer is weak. The model is rarely the bottleneck — the context you feed it is.

We invest first in chunking and metadata: splitting documents on semantic boundaries, attaching source, section and date, and storing enough context per chunk that a retrieved passage stands on its own.

Before shipping, we build an evaluation set from real user questions with known-good answers. Every prompt or retrieval change is scored against it, so we know whether a tweak helped or hurt instead of guessing.

Guardrails come next: structured outputs, a refusal path when confidence is low, and citations back to source documents so users can verify. In production we track accuracy, latency and cost per query on a dashboard the client owns.