How does hybrid search improve RAG retrieval accuracy?
Hybrid search combines dense vector similarity search with traditional sparse keyword search like BM25 to improve retrieval accuracy. Dense vectors excel at semantic understanding, finding conceptually similar content even when exact words differ. Sparse search excels at matching specific terms, names, codes, and identifiers that semantic search might miss. The combination is implemented via reciprocal rank fusion which merges results from both methods by combining their rank positions, or learned fusion where a model weights the contribution of each method. For RAG applications, hybrid search significantly improves retrieval of documents containing technical terminology, product names, or specific identifiers alongside conceptual matches. Most enterprise RAG deployments benefit from hybrid search, with databases like Qdrant, Weaviate, and Elasticsearch providing native hybrid search support.