Introduction to Retrieval-Augmented Generation (RAG)

Large language models are powerful, but they often struggle with hallucination and outdated knowledge. Retrieval-Augmented Generation (RAG) solves this by grounding AI models in your own private or real-time data. In this text-based course, you will transition from understanding basic AI concepts to designing the architecture for reliable, data-driven AI systems. You will learn how to prepare, store, retrieve, and integrate external information to make your AI applications significantly more accurate and contextually aware. What you'll learn: - Understand the core principles of RAG and how it prevents model hallucinations - Explore modern data chunking strategies to prepare text for AI processing - Master the fundamentals of vector databases and semantic search - Apply prompt engineering techniques to ground LLM responses in retrieved context - Evaluate the quality and accuracy of RAG system outputs using modern frameworks - Discover hybrid search methods that combine keyword matching with vector retrieval. You will start by mastering foundational terminology and the core mechanics of RAG. From there, you will progress through the data pipeline—covering document ingestion, vector storage, and query retrieval—before learning how to synthesize these elements into a cohesive, production-ready architecture. This course is designed for beginners, developers, and product managers who want to understand how modern AI systems access external data. No prior background in machine learning or advanced programming is required. Start reading today to build smarter, more reliable AI applications with RAG.