Hybrid Search
A retrieval strategy that combines keyword search (BM25 or full-text) with semantic search (vector embeddings) to get the best of both approaches. Keyword search finds exact matches ('error code 4012'); semantic search finds conceptually similar content ('my payment was declined'). Hybrid search runs both in parallel and merges results using reciprocal rank fusion or weighted scoring. This is the current best practice for RAG-powered AI agents—pure keyword search misses paraphrased queries, and pure semantic search misses specific identifiers.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
A support agent receives 'SKU-7842 keeps showing error 503'. Keyword search matches the exact SKU and error code in the knowledge base. Semantic search finds articles about server timeouts and inventory sync failures. Hybrid search returns the specific SKU troubleshooting article (keyword match) ranked alongside general 503 error resolution steps (semantic match).
Frequently asked questions
- Is hybrid search always better than pure semantic search?
- For most production use cases, yes. Hybrid search handles the 'best of both worlds' problem: exact matches for codes, names, and IDs (where keyword search excels) plus conceptual matches for natural language questions (where semantic search excels). The improvement over pure semantic search is typically 10-25% in retrieval accuracy, especially for technical content with specific identifiers.