How Do You Combine Vector Search and BM25 Results Using Reranking?

What Is the Best Way to Improve Quality in a Hybrid RAG System?

Learn why reranking is the crucial next step in a hybrid RAG system to merge, score, and select the most relevant results from vector and BM25 searches.

Question

You’ve built a hybrid RAG system with both vector search and BM25. You get 20 results from each method. What’s the best next step to improve quality?

A. Send all 40 results to Claude
B. Use reranking to score and select the most relevant results
C. Only use the vector search results
D. Randomly sample from both result sets

Answer

B. Use reranking to score and select the most relevant results

Explanation

When combining multiple search methods (like vector search and BM25), the outputs are distinct lists with incomparable scoring metrics (e.g., BM25 uses unbounded keyword frequency scores, while vector search uses bounded cosine similarity). To improve quality and avoid sending excessive or poorly prioritized data to the LLM (like Claude), the best next step is to use a reranking model or algorithm (such as Reciprocal Rank Fusion or a cross-encoder model) to normalize the scores, merge the lists, and accurately rank the combined results by true semantic relevance, passing only the absolute best chunks to the AI.