Large Language Models are revolutionizing the world right now, but they have significant flaws: their knowledge is not up-to-date, they don't know about your company's specific data, and they start to hallucinate when they don't know the answer to a question.
An often-used technique to counter those points is Retrieval Augmented Generation (RAG), which augments prompts to the LLM with specific knowledge - often fetched from a Vector Database. In this talk, we will explore the steps needed to develop your own RAG system and then apply various steps to make it highly efficient.

This talk is one of several I have on the topic of developing AI-powered applications. It's a bit more advanced and focuses solely on building a highly efficient RAG system. It's geared towards an audience of developers with (at least a little) experience in building AI-powered applications (e.g., with the OpenAI API).