Building Retrieval-Augmented Generation (RAG) Systems

Standard language models often struggle with outdated information or lack access to proprietary data. Retrieval-Augmented Generation (RAG) solves this by connecting generative AI to reliable, external knowledge bases. This text-based course guides you through the core concepts and practical workflows of RAG systems. You will learn how to prepare text data, generate vector embeddings, store them in vector databases, and retrieve the most relevant context to produce precise, hallucination-free AI responses. What you'll learn: - Understand the foundational architecture of RAG systems and how retrieval improves LLM accuracy - Chunk and preprocess text documents to optimize semantic search performance - Generate high-quality vector embeddings using modern embedding models - Configure vector databases to store, index, and query high-dimensional data efficiently - Design effective prompt templates that inject retrieved context into language model queries - Evaluate and optimize RAG performance using basic retrieval metrics and modern evaluation patterns. The course begins with foundational concepts of semantic search and vector space before moving into step-by-step written implementations of document ingestion, indexing, and generation pipelines. This course is designed for software developers, data enthusiasts, and AI beginners who want to build practical AI applications, with no prior experience in vector databases required beyond basic Python familiarity. Start reading today to build smarter, data-grounded AI systems.