Article: Why Vector Search Alone Isn't Enough: Hybrid Retrieval for RAG

Vector embeddings are approximation engines that excel at finding semantically similar content but systematically struggle to distinguish specific entities like version numbers, error codes, and feature flag names. Production queries rarely fall cleanly into purely semantic or purely lexical categories; most are hybrid queries requiring both meaning and exact matches, which is where single-method retrieval fails. BM25 (short for Best Matching 25) is a ranking function that provides the precision that embeddings can't. It uses three mechanisms: inverse document frequency (IDF), which weights rare distinguishing tokens; term-frequency saturation; and document length normalization. Reciprocal Rank Fusion (RRF) combines BM25 and vector results without the pain of score normalization. It operates on rank position alone, rewarding documents that both…