A Comprehensive Guide on RAG: Survey on Retrieval Augmented Generation

This episode provides a detailed examination of Retrieval-Augmented Generation (RAG) for large language models. RAG is a promising solution to enhance the accuracy and credibility of language models by incorporating knowledge from external databases. The episode discusses the progression of RAG paradigms, including Naive RAG, Advanced RAG, and Modular RAG, and examines the retrieval, generation, and augmentation techniques involved. It also introduces metrics and benchmarks for evaluating RAG models and discusses potential research directions. The episode aims to provide a comprehensive understanding of RAG systems and their advancements.