In this informative video, Kelvin Lawrence, Senior Principal Graph Architect at AWS, demonstrates how to integrate Amazon Neptune with large language models using LangChain. He explains the concept of Retrieval Augmented Generation (RAG) and shows how to use natural language queries to interact with graph data. Lawrence walks through creating a Neptune cluster, loading sample data, and using LangChain to generate OpenCypher queries from English questions. He showcases examples of querying airport and route data, highlighting how LangChain can bridge the gap between users unfamiliar with graph query languages and complex graph databases. This integration allows for more intuitive data exploration and showcases the power of combining graph databases with AI language models.