Traditional RAG systems use vector similarity to find relevant document chunks, but struggle with specific keywords or IDs. Hybrid search combines vector similarity with keyword search algorithms like BM25 to overcome these limitations. This approach ensures both semantic relevance and exact keyword matching, dramatically improving retrieval accuracy for diverse query types.

Vector similarity excels at finding semantically related content but often misses specific terms when they're drowned out by more common words. BM25 keyword search solves this by heavily weighting rare terms that appear in queries. When a user searches for a unique identifier or specific keyword, BM25 ensures those documents surface prominently, even if semantically similar alternatives exist.

Implementing hybrid search involves combining both methods with adjustable weighting between semantic and keyword similarity scores. More powerful still is making RAG systems agentic by giving LLMs direct access to retrieval tools. This allows agents to rewrite queries, iteratively fetch information, and dynamically adjust search weighting based on query context. Modern frontier LLMs handle this complexity well, enabling sophisticated retrieval strategies that far outperform static vector-only approaches.