Building production RAG without losing your mind
Eval, retrieval and observability patterns we use in real products.
Most RAG demos hide the hard parts: retrieval quality, evaluation, latency budgets and the long tail of weird user queries. Production systems live or die on these.
We start every RAG project by building an evaluation set before writing a single retriever. Twenty hand-curated query/answer pairs beats a thousand synthetic ones.
Hybrid retrieval (BM25 + vector) consistently wins on real corpora. Tune the weights on your eval set, not on vibes.
Observability is non-negotiable. Log every query, every retrieved chunk, every model call. You cannot improve what you cannot measure.
Finally: cache aggressively. Most user queries are not unique. A simple semantic cache cuts cost and latency dramatically.