GenAI is evolving rapidly and has the potential to transform industries by providing richer user experiences and unlocking new possibilities. The concept of retrieval-augmented generation (RAG) involves combining information retrieval and text generation to deliver personalized and contextual user experiences in real-time. To address challenges such as lack of access to private data, databases play a crucial role in GenAI applications. Databases need to be queryable, flexible, integrated with vector search, and scalable to support the unique demands of GenAI. MongoDB Atlas is considered an ideal database solution for handling multi-modal data, providing a powerful query API, flexible schema design, native vector search indexing, and scalability to support large increases in data volume and requests. With the right database solution, GenAI applications can thrive and deliver accurate, context-aware, and dynamic user experiences that meet the growing demands of today's digital landscape.