RAG Complexity Is a Bet Against the Model

A reported evaluation argues that a minimal RAG architecture—one Postgres table with BM25 and HNSW indexes, hybrid retrieval through reciprocal rank fusion, and an MCP tool server allowing Claude to search iteratively—can rival or exceed more elaborate retrieval pipelines as language models improve. On 500 MuSiQue questions, the system using Claude Haiku achieved 0.418 exact match and 0.564 accuracy, surpassing cited published approaches, while on BRIGHT it reached a mean nDCG@10 of 0.556, placing in the second-to-third leaderboard tier without a fine-tuned retriever. The authors attribute the results to model-directed searching and a disciplined evaluation loop that tests isolated changes using per-query statistics, rejects hypotheses after three unsuccessful variants, and relies on inexpensive database forks to make reversions easy. Most attempted MuSiQue additions, including forced hybrid search, more retrieved results, entity-enriched embeddings, and decomposition prompts, worsened performance, whereas BRIGHT retained corpus-specific prompts and generated “concept sketch” text columns for vocabulary-mismatched domains such as robotics and mathematics. The account acknowledges that the BRIGHT prompts were developed using the evaluated data, that model reasoning rather than retrieval remains a major limitation on multi-hop questions, and that the agent-based approach is much slower and more expensive per query than specialized retrievers, though it argues that falling model costs and lower maintenance needs may make thin, removable architectures increasingly attractive.

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