RagEvaluator
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- An 11-Case Golden Set for Measuring RAG Retrieval Quality
A smart document assistant developer built an offline RAG evaluation using 11 fixed questions, scoring whether an essential answer phrase appears within the top retrieved chunks and its reciprocal rank. The default parent-child chunking, hybrid retrieval, and reranker top-5 configuration achieved recall@1 of 0.455, recall@3 of 0.818, recall@5 of 1.000, and MRR of 0.652; flat chunking produced lower results, while reranker top-2 reduced recall@3 and MRR. Disabling keyword retrieval produced the same aggregate metrics as the default configuration.
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