Your RAG Pipeline Is Missing a 1994 Algorithm

Information retrieval has evolved through methods with different strengths and weaknesses, creating trade-offs at each step. While modern dense retrieval using embeddings excels at understanding semantic meaning (e.g., "broken" vs. "defective"), it can fail to retrieve documents based on exact, rare identifiers like product codes. The BM25 algorithm, developed in 1994, specializes in lexical search, effectively ranking results based on the presence of specific, rare terms while correcting for term frequency and document length. Because neither method is uniformly superior, modern Retrieval-Augmented Generation (RAG) systems benefit from a hybrid approach that combines the semantic power of dense retrieval with the lexical precision of BM25.