A Practical Guide to RAG Pipeline Evaluation

Most teams shipping a retrieval-augmented generation system evaluate it by reading a handful of outputs and deciding they look reasonable. That works until the system is in front of real users, at which point "looks reasonable" stops being a useful signal.

We evaluate RAG pipelines against three separate axes: retrieval precision (are the retrieved chunks actually relevant to the query), groundedness (does the generated answer only claim what the retrieved context supports), and latency under the token budget the product actually has. Optimizing only for one — usually groundedness, because it's the most visible failure mode — tends to quietly wreck one of the other two.

The practical difference this makes: a pipeline that looks great on a curated set of test questions can fail badly on the long tail of real queries, specifically because retrieval precision was never measured separately from the generation quality people were eyeballing.

Our baseline for any RAG engagement now includes a held-out query set built from real user questions (not synthetic ones), scored on all three axes before we call an iteration done — not after a demo goes well.