RAG in 2024: Advancing to Agents

I'm Laurie, VP of Developer Relations at Llama Index. If you've spent time with LlamaIndex, you already know about the importance of retrieval-augmented generation or RAG. In this video, I make the case that while RAG is necessary, it's not enough for sophisticated knowledge retrieval. You need to build an agent. In this video we cover: * Basic RAG * Agentic components, including * Routing * Memory * Planning * Tool use * Agentic reasoning, including * Sequential (like Chain of Thought) * DAG-based * Tree-based (like Tree of thought) * And we briefly cover further extensions including * Observability * Controllability * Customizability You can find links to all the resources covered in this video at https://bit.ly/li-agent-resources