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Benchmarked RAG performance using LangChain, DSPy, and Crusor with internal tools
A comprehensive research platform dedicated to evaluating and optimizing Retrieval-Augmented Generation (RAG) systems. This project implements and compares multiple RAG approaches using industry-leading frameworks like LangChain and DSPy. The platform includes custom evaluation metrics, benchmarking tools, and integration with internal systems for real-world testing. It provides detailed performance analytics, helping identify the most effective strategies for different use cases. The research has contributed valuable insights into RAG optimization and prompt engineering.