Retrieval-Augmented Generation (RAG) has become a cornerstone for building knowledge-intensive LLM applications. However, relying solely on dense vector embeddings can lead to failures when user queries contain rare proper nouns, product codes, or exact phrases. In such cases, lexical search methods like BM25 excel at exact token matching, while semantic embeddings capture intent.
Hybrid search addresses this gap by merging results from both sparse and dense retrievers. A typical implementation uses Reciprocal Rank Fusion (RRF) to blend rankings without needing manual weight tuning. In our benchmarks on a technical support corpus, hybrid retrieval improved answer relevance by 18% over pure vector search, particularly for queries containing error codes.
When engineering these systems, consider the latency overhead of dual pipelines. Utilizing a single index that supports both keyword and vector queries—such as Elasticsearch with dense_vector fields—can simplify deployment. Ultimately, hybrid search is a pragmatic step toward production-grade RAG that doesn't require larger models, just smarter retrieval.