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This article explains AutoRAG, a framework designed to improve Retrieval-Augmented Generation (RAG) systems by treating their design as an optimization problem. It highlights the importance of evaluating entire pipelines rather than focusing on isolated components, emphasizing how effective query reformulation and context expansion enhance answer quality.