Data Engineering and RAG for Generative AI on AWS

Generative AI models are only as good as the data they can access. To build truly reliable, hallucination-free AI applications, you must understand how to prepare, store, and retrieve information effectively. This structured text-based course guides you through the essential concepts of data preparation, vector embeddings, and retrieval-augmented generation (RAG) architectures on AWS. You will start by learning foundational data engineering terminology, storage types, and pipeline concepts. From there, you will explore how to connect external knowledge bases to large language models to ensure accuracy and context-aware responses. What you'll learn: - Understand the core principles of data engineering, pipelines, and storage solutions for AI systems. - Learn the fundamentals of Retrieval-Augmented Generation (RAG) and how it prevents AI hallucinations. - Explore vector databases, chunking strategies, and semantic search techniques. - Configure robust data ingestion and retrieval workflows using key AWS services. - Practice designing clean architectures that connect language models to external data sources. - Evaluate RAG system performance and understand modern observability patterns. Through clear written explanations, architectural breakdowns, and conceptual self-assessment exercises, you will gain a solid mental model of modern AI data infrastructure. This course is designed for beginners, software developers, and aspiring data professionals who want to understand the data-driven side of generative AI with no prior machine learning experience required. Start reading today to build smarter, more reliable AI systems.