In the world of Retrieval-Augmented Generation (RAG), developers often face a critical decision: should they use sparse keyword search or dense vector search? While vector search has become popular for its semantic capabilities, keyword search remains highly effective in specific scenarios.
Keyword search, powered by algorithms like BM25, excels when queries contain exact terms or rare phrases. For example, searching for a specific product code, legal clause, or technical identifier often yields better results with keyword matching. It is also more efficient for small-to-medium datasets and requires less computational overhead than vector search.
On the other hand, vector search shines when dealing with synonyms, paraphrasing, or ambiguous queries. It captures semantic meaning, making it ideal for open-ended questions or diverse content.
A hybrid approach—combining both methods—often provides the best balance, leveraging the precision of keywords and the flexibility of vectors. In production RAG systems, understanding when to use each technique is key to optimizing retrieval quality and system performance.