RAG & Retrieval
Hybrid Search
A retrieval strategy that combines keyword-based search (BM25, TF-IDF) with semantic vector search (embeddings) to get the best of both approaches.
In depth
A retrieval strategy that combines keyword-based search (BM25, TF-IDF) with semantic vector search (embeddings) to get the best of both approaches. Keyword search excels at exact matches and rare terms. Semantic search excels at finding conceptually related content. Hybrid search runs both in parallel and merges the results using reciprocal rank fusion or similar techniques. It consistently outperforms either approach alone in RAG systems.
Example
In practice, developers reach for Hybrid Search when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
What is Hybrid Search?
A retrieval strategy that combines keyword-based search (BM25, TF-IDF) with semantic vector search (embeddings) to get the best of both approaches.
Why does Hybrid Search matter for AI developers?
Hybrid Search sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Hybrid Search?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Hybrid Search. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
RAG & Retrieval
Semantic Search
A search method that finds results based on meaning rather than exact keyword matches.
RAG & Retrieval
Embeddings
Numerical vector representations of text that capture semantic meaning.
RAG & Retrieval
Retrieval
The process of finding relevant documents, passages, or data from a knowledge base in response to a query.
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