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RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets

arXiv — cs.CL•Tuesday, November 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets

RAGSmith is an exciting new framework designed to optimize Retrieval-Augmented Generation (RAG) methods by treating the design process as an end-to-end architecture search. This innovative approach allows for the exploration of numerous configurations, making it easier to find the best combinations for various datasets. The significance of RAGSmith lies in its ability to enhance the quality of RAG by addressing the complexities of module interactions, ultimately leading to more effective and efficient generation methods.
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