The BEST component for your RAG system

In this session, Jeffrey Kim discusses the critical importance of optimizing Retrieval-Augmented Generation (RAG) systems. He introduces AutoRAG, an open-source tool designed to automatically optimize RAG pipelines for specific data and use cases, leading to improved RAG performance. The talk addresses the challenge of selecting the best RAG components and evaluation data, emphasizing the "human-in-the-loop" strategy as a realistic and effective approach. AutoRAG streamlines the process of evaluating various RAG module combinations and finding the optimal pipeline using quantitative metrics. The presentation also covers how to configure AutoRAG, interpret its results, and various ranking and generation metrics for effective RAG evaluation.