Retrieval-augmented generation (RAG) has become a significant architectural pattern in the enterprise landscape, thanks to the rapid growth of generative AI, allowing developers to leverage pre-trained large language models (LLMs) with real-time, company-specific data to create AI-powered applications that deliver accurate and up-to-date outputs. This innovation bypasses the need for specialized data science teams to retrain or fine-tune models, thereby saving time and resources. The "Building AI with MongoDB" blog series showcases various applications of RAG, including conversational AI, threat intelligence, contract management, and healthcare compliance, highlighting the use of MongoDB Atlas Vector Search in these contexts. Notable case studies include Eni's use of MongoDB Atlas to make geological data actionable for decision-making, Potion's video personalization at scale for sales professionals, and Kovai's integration of Vector Search to enhance its knowledge base platform. These examples underscore the increasing adoption and versatility of RAG in enhancing data-driven decision-making and user engagement across different sectors.