Retrieval-Augmented Generation (RAG) Fundamentals for AI Applications

Standard Large Language Models often struggle with outdated knowledge and hallucinated facts when applied to private or real-time data. Retrieval-Augmented Generation (RAG) solves this by connecting LLMs to external data sources, ensuring precise and verifiable outputs. This text-based course guides you through the foundational mechanics of RAG systems, helping you transition from working with static models to building dynamic, data-connected AI workflows. What you'll learn: - Understand the core architecture of RAG and how it differs from model fine-tuning - Explore document chunking strategies and embedding models to represent text numerically - Configure vector databases to store and query your domain-specific data efficiently - Apply prompt engineering techniques to ground LLM responses in retrieved context - Analyze evaluation methods to measure the accuracy and relevance of generated answers You will start with key definitions and core concepts before exploring the step-by-step pipeline of data preparation, vector retrieval, and prompt synthesis through clear written explanations and practical code snippets. This course is designed for software developers, product managers, and AI enthusiasts who are new to RAG. No prior experience with vector databases or complex machine learning is required. Start reading today to build smarter, data-driven AI solutions.