Building Retrieval-Augmented Generation (RAG) Systems

Large language models are powerful, but they often lack access to your specific, private, or real-time data. Retrieval-Augmented Generation (RAG) solves this by connecting these models to external knowledge bases to produce accurate, grounded answers. In this course, you will transition from understanding basic language models to designing and implementing your own RAG systems. You will gain the skills to query vector databases, inject relevant context into prompts, and evaluate the accuracy of your system's outputs. What you'll learn: - Understand the fundamental concepts of RAG, language models, and vector embeddings. - Configure vector databases to store, index, and retrieve semantic data efficiently. - Apply prompt engineering techniques to ground model responses in retrieved context. - Build end-to-end RAG pipelines that connect external documents to text-generation models. - Evaluate RAG system performance using modern metrics to ensure accuracy and reduce hallucinations. This text-based course begins with essential terminology and foundational architecture before guiding you through step-by-step code implementations and practical design patterns. You will read clear explanations, analyze structured code snippets, and complete written exercises to solidify your understanding. This course is designed for software developers, data enthusiasts, and AI beginners who want to build context-aware applications. No prior experience with vector databases or generative AI is required. Start reading today to build smarter, data-driven AI applications.