Foundations of Retrieval-Augmented Generation (RAG)

Large Language Models are incredibly powerful, but they often suffer from outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG) solves this by anchoring AI models to your own verified data sources. In this text-based course, you will learn how to design and understand RAG architectures from the ground up, bridging the gap between static generative models and dynamic, real-time information systems. What you'll learn: • Understand the core mechanics of Large Language Models and why retrieval-augmented systems are necessary. • Explore the fundamentals of document ingestion, text chunking, and embedding generation. • Learn how vector databases store and retrieve semantic information efficiently. • Apply prompt engineering techniques to synthesize retrieved data into clear, coherent answers. • Analyze modern RAG patterns, including semantic search, reranking, and basic system evaluation. You will start by exploring foundational terminology and the basic architecture of generative AI, progressing through written explanations and code snippets covering text embeddings and integration strategies. This course is designed for software developers, data enthusiasts, and AI beginners who want to understand how modern search-and-generate systems work. No prior experience with vector databases is required. Start reading today to unlock the potential of context-aware AI.