Retrieval-Augmented Generation (RAG) for Language Models

Large language models are powerful, but they often struggle with outdated training data and factual hallucinations. Retrieval-Augmented Generation (RAG) solves this critical limitation by securely connecting models to external, verified data sources in real time. In this course, you will transition from understanding basic text generation to designing robust RAG workflows. You will learn how to enrich model prompts with relevant, domain-specific information, ensuring your AI systems produce accurate, verifiable, and context-aware responses. What you will learn: Understand the core architecture of RAG and how it prevents model hallucinations; Explore document chunking strategies and text embedding techniques to prepare your data; Configure vector databases to perform efficient semantic searches on custom knowledge bases; Design prompt templates that successfully integrate retrieved context with user queries; Apply evaluation techniques to measure the accuracy and relevance of your RAG system's outputs. The course begins with foundational definitions and key terminology of language models and information retrieval. You will then progress through the step-by-step mechanics of data ingestion, retrieval, and generation, working through written explanations and conceptual exercises. This course is designed for software developers, product managers, and AI enthusiasts who are new to retrieval technologies. No advanced machine learning background is required. Start reading today to unlock the power of context-aware artificial intelligence.