Hybrid Retrieval
Hybrid retrieval combines semantic vector search with lexical keyword search to retrieve relevant passages. Vector search captures meaning and paraphrase, keyword search captures exact terms and identifiers, and a fusion step merges both result sets so neither precise tokens nor conceptual matches are missed.
Synonyms: hybrid search, dense-sparse retrieval, vector plus keyword search, fusion retrieval
Hybrid retrieval acknowledges that no single retrieval signal is enough for production quality. Dense vector embeddings excel at conceptual similarity but blur exact identifiers; sparse lexical methods nail precise tokens but ignore meaning. By running both and fusing their rankings, hybrid retrieval improves recall without sacrificing precision, which matters when an answer must be grounded in exactly the right passage. A reranking pass over the fused candidates then sharpens the final context handed to the model.