A Survey of Production RAG Pain Points and Solutions // Jerry Liu // AI in Production Conference

// Abstract
Large Language Models (LLMs) are revolutionizing how users can search for, interact with, and generate new content. There's been an explosion of interest around Retrieval Augmented Generation (RAG), enabling users to build applications such as chatbots, document search, workflow agents, and conversational assistants using LLMs on their private data. While setting up naive RAG is straightforward, building production RAG is very challenging. There are parameters and failure points along every stage of the stack that an AI engineer must solve in order to bring their app to production. This talk will cover the overall landscape of pain points and solutions around building production RAG, and also paint a picture of how this architecture will evolve over time.

// Bio
Jerry is the co-founder/CEO of LlamaIndex, the data framework for building LLM applications. Before this, he has spent his career at the intersection of ML, research, and startups. He led the ML monitoring team at Robust Intelligence, did self-driving AI research at Uber ATG, and worked on recommendation systems at Quora.

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#RAG #llm #production