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