Building RAG Systems from Scratch

Large Language Models are incredibly powerful, but they often lack access to your specific, private data. Retrieval-Augmented Generation (RAG) solves this by connecting LLMs to external knowledge sources to deliver accurate, context-aware answers. This text-based course guides you through the process of building your own functional RAG applications from the ground up. You will gain a clear, conceptual and practical foundation in how to prepare data, manage embeddings, and query vector databases. What you'll learn: - Understand the core architecture of RAG systems and how retrieval enhances LLM generation. - Prepare and chunk raw text data effectively to maintain semantic meaning. - Generate text embeddings and store them in modern vector databases. - Implement semantic search queries to retrieve highly relevant context. - Apply prompt engineering patterns to combine retrieved data with LLM queries. - Evaluate RAG system outputs for accuracy, relevance, and hallucination prevention. The course begins with essential AI terminology and architectural concepts before guiding you through data preparation, embedding generation, vector storage, and final LLM integration. You will learn through clear written explanations, structured code walkthroughs, and conceptual exercises. This course is designed for beginner developers and AI enthusiasts who want to understand RAG from the ground up. No prior experience with vector databases or generative AI APIs is required, though a basic familiarity with Python is helpful. Start reading today to build your first data-connected AI application.