The video walks through building a full Retrieval‑Augmented Generation (RAG) system using LangGraph, a library that structures LLM workflows as directed graphs. It starts by defining the core nodes: a retriever that queries a vector store, a generator that produces answers, and a router that decides when to call the retriever versus when to rely on the LLM’s internal knowledge. The presenter shows how to create LangGraph State objects to pass documents, query strings, and intermediate results between nodes, and how to configure edge conditions for loops such as re‑ranking or follow‑up retrieval. They demonstrate integrating LangChain’s vectorstores, embeddings, and memory modules, then wrapping them in LangGraph components. The pipeline includes a fallback chain that triggers a web‑search tool if the vector store returns low relevance, and a self‑critique node that asks the LLM to verify its own answer. Finally, they cover deployment considerations: serializing the graph, handling async calls, and monitoring token usage. The end result is a modular, extensible RAG architecture that can be customized with additional tools, supports iterative refinement, and maintains clear separation of concerns via LangGraph’s graph abstraction.