Learn to implement Retrieval Augmented Generation (RAG) using Meta's open-source Llama Stack project in this 13-minute tutorial video. Discover how to enable large language models to reference external knowledge beyond their training data by setting up a local Llama Stack server with Podman, creating and ingesting documents into a vector database, and building a RAG agent that selectively retrieves context from your data. Master the process of chatting with real documents like PDFs, invoices, or project files using Agentic RAG, and understand how RAG integrates unique data into AI workflows while leveraging Llama Stack's scalability from local development to production deployment on Kubernetes.