MongoDB Atlas and Amazon Bedrock make it incredibly easy and safe to simplify bringing enterprise data to generative AI using retrieval-augmented generation (RAG). RAG enables organizations to build hyper-personalized experiences uniquely tailored to business needs using enterprise data.
MongoDB Atlas securely unifies real-time, operational, unstructured, and vectorized data, removing the friction of integrating the essential data components required to give large language models (LLMs) the context they need in a RAG workflow.
At the same time, Amazon Bedrock offers a fully managed, end-to-end RAG workflow feature alongside a range of foundation models (FM) and tools for creating, training, and deploying generative AI solutions.
Here, we explore how they work together to accelerate time to value for enterprises looking to build generative AI experiences that take advantage of their proprietary data.
