Originally published on AI Tech Connect.
What you need to know Most RAG systems that fail in production do not fail at the language model. They fail at retrieval — the right chunk was never fetched, or it was fetched and then buried at rank 40 where the generator never saw it — and the team has no trace to prove what went wrong. This guide is the architecture that fixes both problems. Three things to take away before we go deep: Hybrid retrieval is the 2026 baseline. Run lexical BM25 and dense semantic search in parallel and fuse the two ranked lists with reciprocal rank fusion. Published benchmarks put the lift at roughly +8 percentage points on Recall@5 over BM25 alone on text-and-table corpora. A reranker is your highest-leverage precision stage. A cross-encoder over the top 50 to 100 fused candidates lifts Recall@5 from…
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