Building AI with MongoDB: Retrieval-Augmented Generation (RAG) Puts Power in Developers’ Hands

Building AI with MongoDB: Retrieval-Augmented Generation (RAG) Puts Power in Developers’ Hands` presents the benefits of retrieval-augmented generation (RAG), a powerful combination that allows developers to build AI-powered apps grounded in enterprise data and knowledge, without specialized data science teams. This approach enables accurate, up-to-date, and relevant outputs, achieved through pre-trained general-purpose LLMs fed with real-time company-specific data. The use of MongoDB Atlas Vector Search facilitates this process, providing a robust, cost-effective, and blazingly fast solution for developers to build AI-driven semantic search and RAG experiences. Three novel use cases are highlighted: Eni's geological data unlocking, Potion's video personalization at scale, and Kovai's bringing power of Vector Search to enterprise knowledge bases. These examples showcase the growing adoption of RAG in the enterprise landscape, with MongoDB Atlas playing a crucial role in enabling developers to build AI-powered applications efficiently.

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