Hybrid Search RAG Cuts Missed Retrievals by 30–40% in Internal Doc Systems

A production guide published on DEV Community details how combining vector and keyword search in a Retrieval-Augmented Generation (RAG) pipeline significantly improves retrieval accuracy over internal documents. The approach runs dense vector search (embeddings) and sparse keyword search (BM25 via Elasticsearch) in parallel, then fuses their normalized scores to surface the most relevant results. One team at LogicLoop reported a 22% improvement in MRR@10 after switching from vector-only to hybrid search on their engineering wiki, with benchmark tests across 500,000 Confluence pages and Jira tickets showing hybrid search achieving an MRR@10 of 0.50 versus 0.41 for vector-only. The tradeoff includes roughly 30ms of additional latency and doubled storage overhead from maintaining two indexes simultaneously. A notable edge case was identified for very short queries of one to two words, where keyword search tends to dominate, prompting the team to route such queries to keyword-only search via a simple length check.