This YouTube lecture presents a mathematical equation for Retrieval Augmented Generation (RAG): Query + Prompt + Context + Large Language Model (LLM). The presenter explains how each component contributes to improved LLM responses, reducing hallucinations by providing relevant context from a database. The lecture details the indexing process (loading, splitting, embedding, storing data) and retrieval methods, illustrating with examples and showcasing advanced RAG techniques like multi-query retrieval and contextual compression. Various tools and resources for implementing RAG are also discussed.
