Retrieval-Augmented Generation (RAG) Systems in Practice

Large Language Models are incredibly powerful, but they often struggle with hallucination and outdated information. Retrieval-Augmented Generation (RAG) solves this by anchoring models to real-time, external data sources. This text-based course guides you through the foundational concepts and step-by-step implementation of RAG pipelines. You will transition from understanding basic language model limitations to writing clean, structured code that retrieves, processes, and integrates custom documents to generate accurate, context-aware answers. In this course, you will: 1. Understand the core architecture of Retrieval-Augmented Generation and how it differs from fine-tuning. 2. Process and chunk raw text documents efficiently to prepare them for embedding models. 3. Configure vector databases to store, index, and retrieve relevant data. 4. Apply prompt engineering patterns to guide language models using retrieved context. 5. Implement basic evaluation strategies to measure retrieval relevance and generation quality. You will begin with essential terminology and structural concepts before moving into step-by-step coding patterns for document ingestion, storage, retrieval, and response generation. Designed for software developers, data enthusiasts, and technical beginners eager to build AI applications, this course requires no prior AI experience beyond basic Python familiarity. Start reading today to build smarter, data-connected AI applications.