Why Exact-Match Queries Break RAG Systems and How Hybrid Search Fixes It

Retrieval-Augmented Generation (RAG) systems built on vector search often fail when users search for exact terms like product codes, error numbers, or ticket IDs, because embeddings rank by semantic meaning rather than literal token matching. This gap typically goes undetected during testing, since semantic queries perform well in demos, but erodes user trust once deployed in real-world scenarios. Upgrading to a larger embedding model does not resolve the issue, as the root cause is a fundamental mismatch between exact-match retrieval and semantic similarity search. Hybrid search — combining BM25 keyword search with vector queries and merging results via Reciprocal Rank Fusion — is identified as the most impactful fix for most projects. Beyond hybrid search, further improvements can be achieved through cross-encoder reranking, query expansion, metadata filtering, and graph-based retrieval for complex entity-relationship queries.