The Llama Stack Tutorial: Episode Three - Llama Stack & RAG: Chat with your documents

Building AI apps is one thing—but making them chat with your documents is next-level. In Part 3 of the Llama Stack Tutorial, we dive into Retrieval Augmented Generation (RAG), a pattern that lets your LLM reference external knowledge it wasn't trained on. Using the open-source Llama Stack project from Meta, you'll learn how to:- Spin up a local Llama Stack server with Podman- Create and ingest documents into a vector database- Build a RAG agent that selectively retrieves context from your data- Chat with real docs like PDFs, invoices, or project files, using Agentic RAGBy the end, you'll see how RAG brings your unique data into AI workflows and how Llama Stack makes it easy to scale from local dev to production on Kubernetes.