BM25

BM25 (Best Matching 25) is a probabilistic ranking function from the 1990s that scores documents by how well their terms match a query, weighted by term frequency and inverse document frequency. In modern RAG systems, BM25 is paired with dense vector search (the "hybrid" pattern) because each catches what the other misses — BM25 nails exact terms and proper nouns; dense retrieval nails semantic similarity. Pure vector search loses to hybrid in almost every benchmark.