Hybrid Search
Combining dense (semantic) retrieval with sparse (keyword / BM25) retrieval for better recall.
Hybrid search runs both a dense retriever (embeddings) and a sparse retriever (BM25 or similar) and fuses their results — typically via Reciprocal Rank Fusion or a weighted score.
Dense retrieval captures meaning; sparse retrieval catches exact matches like proper nouns, product codes, dates and rare tokens. Neither alone is complete for enterprise search.
Every serious production RAG system in 2026 uses hybrid retrieval. It's a small ops addition for a meaningful accuracy lift, especially on technical and structured content.
Key points
- Dense + sparse combined via RRF or weighted fusion
- Catches both semantic matches and exact-token matches
- Especially valuable for technical, code, or catalogue content
- Small latency addition, meaningful recall uplift
Common use cases
Frequently asked
Related terms
A pattern that grounds LLM answers in your private documents by retrieving relevant snippets at query time.
A database optimized for storing and searching high-dimensional embeddings by similarity.
A second-stage model that re-scores retrieved candidates to improve final ranking quality in RAG.
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