How Adaptive RAG Works Without Needing an LLM at All

▤ Summary

This article presents an adaptive RAG approach that reduces or removes dependence on an LLM for retrieval decisions. The method uses question features such as type, complexity, popularity, frequency, and knowledgability to decide when to retrieve and how much to retrieve. The authors report strong accuracy and efficiency results across several QA benchmarks, with far fewer LLM calls than competing methods. The work targets search and retrieval workflows rather than a packaged commercial product, so it reads as a research update for AI and information retrieval teams.

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