Large Language Models (LLM’s) are starting to revolutionize how users can search for, interact with, and generate new content. Some recent stacks and toolkits around Retrieval Augmented Generation (RAG) have emerged where users are building applications such as chatbots using LLMs on their own private data. This opens the door to a vast array of applications. However while setting up a naive RAG stack is easy, there is a long-tail of data challenges that the user must tackle in order to make their application production-ready.
In this talk, we give practical tips on how to manage data for building a robust/reliable LLM software system, and how LlamaIndex provides the tools to do so.