In this talk, we will explore the cutting-edge techniques for Retrieval Augmented Generation or RAG with MongoDB. We will focus on leveraging Atlas Vector Search with the rich document model in MongoDB to improve RAG. We will show how to build a RAG system using a Parent Child Retrieval Strategy to enable more efficient and accurate retrieval of relevant information. Additionally, we will show how this can be done within the MongoDB document model rather than implementing these relationships in the application layer. And finally, we will introduce the concepts of Quantization and Search Nodes, two capabilities native to MongoDB, which enable you to serve vector workloads at scale. This talk is aimed at developers, ML engineers, and data scientists interested in building AI powered experiences with RAG. By the end of the session, attendees will have a solid understanding of how Retrieval Augmented Generation, Vector Search, and MongoDB can be leveraged to build innovative and scalable AI-powered applications.