This podcast episode explores the concept of Retrieval Augmented Generation (RAG) in the context of large language models (LLMs). RAG combines retrieval and generation techniques to improve the quality and accuracy of text generated by LLMs. It addresses the limitations of LLMs by injecting specific context and data sources into the prompt, resulting in more relevant and accurate responses. The episode discusses the benefits, challenges, and alternatives of using RAG systems, emphasizing the need for caution and providing suggestions for reducing hallucinations and improving accuracy.
