Adding Agentic Layers to RAG

In this talk, Jerry Liu, Co-Founder of LlamaIndex, dives into the world of Retrieval Augmented Generation (RAG) and discusses how to incorporate agents into the RAG framework. He introduces LlamaIndex, a data framework for building LLM applications, and explains the limitations of naive RAG prototypes. Jerry explores the challenges with naive RAG and presents solutions for handling complex questions, including summarization, comparison, structured analytics, and multipart inquiries. He further delves into the concept of agents, their role in utilizing LLMs for automated reasoning and tool selection, and the different layers at which they can be added to the RAG pipeline. From basic routing and query planning to tool use and agentic loops, Jerry showcases a range of agentic reasoning methods. He also touches on the exciting possibilities of long-term planning agents that optimize system-level components. Throughout the talk, he emphasizes the importance of observability, control, and customizability in building effective agents. If you're interested in understanding how to enhance your RAG applications with agentic layers and explore various agent paradigms, this talk provides valuable insights and practical tips. Check out LlamaIndex's documentation for more information. #AI #RAG #Agents