Retrieval-Augmented Generation (RAG) with AWS Bedrock

Generative AI models are incredibly powerful, but they often struggle with hallucinations and lack access to your specific, private data. Retrieval-Augmented Generation (RAG) solves this by connecting foundational models to external knowledge bases, ensuring highly accurate and context-aware responses. In this text-based course, you will learn how to design and implement robust RAG workflows using AWS Bedrock. You will start with the fundamental concepts of embeddings and vector databases, progress to chunking strategies, and ultimately construct a complete architecture that retrieves relevant information to ground your AI's responses. What you'll learn: - Understand the core architecture of Retrieval-Augmented Generation (RAG) and how it reduces AI hallucinations. - Configure AWS Bedrock to access foundational models and manage API orchestrations. - Implement data chunking strategies and document processing pipelines to prepare your knowledge base. - Utilize vector databases and embeddings to store and retrieve semantic information efficiently. - Apply prompt engineering techniques to ground model responses using retrieved context. - Evaluate the performance and accuracy of your RAG system using modern evaluation patterns. The course begins with essential terminology, core definitions, and foundational concepts before guiding you through step-by-step written explanations of RAG architectures and Bedrock integrations. You will practice your skills through conceptual exercises and written code walkthroughs designed to build practical confidence. This course is designed for software developers, data enthusiasts, and AI beginners who want to build context-rich AI applications without needing a background in deep learning. No prior experience with AWS Bedrock or generative AI architecture is required. Start reading today to unlock the power of grounded generative AI with AWS Bedrock.