Day 03/90: Hybrid Search in RAG: Can Combining BM25 and Vector Search Find Better Evidence?
Your RAG app retrieves five chunks. They look relevant, but none actually answers the question. The LLM responds confidently anyway. The result? A fluent answer built on the wrong evidence. Before changing the prompt or switching models, ask: Did the retriever find the evidence needed to answer? That’s the question I’ll explore by comparing BM25, vector search, and hybrid retrieval with…
On day three of a 90-day investigation, this article explores whether merging BM25 keyword matching and vector search can improve the accuracy of Retrieval-Augmented Generation (RAG) systems. Both methods have distinct strengths and weaknesses.
BM25 excels at extracting exact error codes, product names, and technical terms, but it may struggle with understanding nuanced queries. Vector search, on the other hand, can identify similar passages even when the wording differs, but it risks ranking general information above specific solutions.
A promising approach is to combine both methods using Reciprocal Rank Fusion (RRF). This technique merges the rankings from keyword matching and semantic similarity, assigning a fused score to each chunk. Chunks appearing near the top of multiple lists tend to receive strong scores. However, RRF doesn't verify if a chunk actually answers the question.
In the proposed pilot, the author will compare three strategies—BM25 alone, vector search alone, and hybrid retrieval using RRF—on a document collection with 15-20 questions. The evaluation metrics include Hit@5 (whether a supporting chunk appears in the top five results) and MRR@5 (the ranking position of the first supporting chunk). The pilot will also measure retrieval latency and document failure patterns.
Ultimately, the goal is to determine whether hybrid search truly enhances RAG performance, requiring thorough measurement and inspection of missed queries before attributing failures solely to the LLM.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
