How Retrieval-Augmented Generation (RAG) Works on AWS

How Retrieval-Augmented Generation (RAG) Works on AWS Generative AI models are powerful, but they have an important limitation: they only know what they were trained on. When you want an AI system to answer questions about your own documents, company knowledge bases, or internal data, relying solely on the model’s training data is not enough. This is where Retrieval-Augmented Generation (RAG) becomes one of the most important architectural patterns in modern AI systems. RAG allows generative AI models to access external knowledge sources in real time. Instead of guessing or relying only on training data, the model retrieves relevant information and then generates an answer based on that data. In this article, we will explore what RAG is, why it matters, and how it can be implemented using AWS services to build scalable and production-ready AI systems. What is Retrieval-Augmented Generation (RAG)? Retrieval-Augmented Generation is an AI architecture that combines information retrieval w