Three Misconceptions About RAG

In this video, Philipp Krenn, Head of DevRel & Developer Advocate at Elastic, discusses three common misconceptions about Retrieval Augmented Generation (RAG).

RAG is a concept that involves providing additional context to language models to improve their ability to generate accurate and relevant answers. Philipp explains how RAG works, the importance of context in generating better answers, and the role of vector search and keyword search in the process. He also explores the use of LLMs (Language Model Models) in search and retrieval, as well as their potential applications in query reformulation and generating test data. Whether you're a marketer, designer, or developer, this talk offers valuable insights into the fundamentals and potential applications of RAG. #AIForDevelopers