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.

• AutoRAG treats RAG pipeline design as a search/optimization problem over components (chunking, query formulation, retrieval, context presentation) rather than something you configure by intuition.
• Query decomposition breaks complex queries into simpler sub-queries to improve precision of fact retrieval.
• Combining dense embeddings with sparse methods like BM25 in a hybrid approach outperforms relying on either alone.
• Passage augmentation (pulling in adjacent passages, not just top hits) and prompt structuring to counter the "Lost in the Middle" effect both meaningfully improve answer quality.