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What is hybrid search and why is it often better than pure vector search?

For AI / LLM EngineerML Engineer
The short answer

Hybrid search combines dense vector similarity with sparse keyword search such as BM25, then fuses the rankings. Dense retrieval captures semantic meaning while keyword search nails exact terms, identifiers, and rare tokens, so combining them improves recall and precision over either alone.

How to think about it

Hybrid search combines dense vector similarity with sparse keyword search such as BM25, then fuses the rankings. Dense retrieval captures semantic meaning while keyword search nails exact terms, identifiers, and rare tokens, so combining them improves recall and precision over either alone.

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Keep practising

What is hybrid search and when should you use semantic vs keyword retrieval? What is a vector database and how does it enable semantic retrieval? What are embeddings, and how do you measure similarity between them for vector search? Why do dense word embeddings outperform one-hot vectors? When should you use grid search vs random search vs Bayesian optimisation for hyperparameter tuning?
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