What is Hybrid RAG?
Hybrid RAG is a retrieval-augmented generation approach that combines dense retrieval (embedding-based semantic search) with sparse retrieval (keyword-based methods like BM25) to leverage the complementary strengths of both techniques. The system performs both types of retrieval in parallel or sequence, then merges the results using ranking algorithms to provide a final set of retrieved documents that benefits from both semantic understanding and keyword precision.
Dense retrieval excels at understanding semantic similarity and can find relevant content even when query and document don't share exact terms, but may struggle with rare words, proper nouns, or highly specific terminology. Sparse retrieval is excellent at exact matching and handling domain-specific terms but misses semantically similar content with different wording. By combining both approaches, hybrid systems achieve better overall retrieval quality across diverse query types.
The result merging typically uses algorithms like Reciprocal Rank Fusion (RRF) that combine rankings from different retrievers without requiring score normalization, or more sophisticated re-ranking models that evaluate candidates from both retrieval methods. Hybrid RAG has become a best practice in production systems, with many vector databases now offering built-in hybrid search capabilities. The approach provides more robust retrieval that adapts to various query patterns without requiring manual selection of the retrieval strategy.