{"id":3614,"date":"2026-10-18T21:28:04","date_gmt":"2026-10-18T15:58:04","guid":{"rendered":"https:\/\/semai.ai\/blogs\/how-to-evaluate-rag-retrieval-for-dense-sparse-and-hybrid-search\/"},"modified":"2026-10-18T21:28:04","modified_gmt":"2026-10-18T15:58:04","slug":"how-to-evaluate-rag-retrieval-for-dense-sparse-and-hybrid-search","status":"publish","type":"post","link":"https:\/\/semai.ai\/blogs\/how-to-evaluate-rag-retrieval-for-dense-sparse-and-hybrid-search\/","title":{"rendered":"How to evaluate RAG retrieval for dense, sparse, and hybrid search"},"content":{"rendered":"
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RAG retrieval evaluation determines whether a language model receives the right evidence before it generates an answer <\/a> . A useful evaluation separates retrieval quality from generation quality, tests dense and sparse methods against the same ground-truth set, and measures whether hybrid search improves relevant evidence without adding disproportionate latency or maintenance work.<\/p>\n

The central decision is not whether one retrieval method is universally superior. It is whether the method returns the passages that answer the query, at useful ranks, across the vocabulary and document patterns found in the organization\u2019s corpus.<\/p>\n

What question should a RAG retrieval evaluation answer?<\/h2>\n

RAG retrieval evaluation measures whether a retriever returns relevant passages for a given query at an acceptable rank. This isolates the evidence-selection layer from prompt construction, model reasoning, and answer wording. A retrieval system that misses the required passage cannot reliably recover through generation alone.<\/p>\n

Define the decision before selecting metrics <\/a> : improve coverage, improve rank order, reduce irrelevant context, or control retrieval cost. That decision determines whether Recall@k, Mean Reciprocal Rank, Normalized Discounted Cumulative Gain, or a combination provides the clearest signal.<\/p>\n