Retrieval-Augmented Generation (RAG) is a cutting-edge approach that enhances language models by integrating external knowledge retrieval, improving response accuracy and relevance. This workshop introduces the fundamentals of RAG, exploring its key techniques, architectures, and practical applications. Participants will learn how RAG-powered systems work, the role of vector databases, and popular frameworks like LangChain and LlamaIndex. Through hands-on exercises, attendees will gain insights into building and optimizing RAG-based solutions for various use cases, from chatbots to document summarization. This session is ideal for AI enthusiasts, developers, and data scientists looking to leverage RAG for enhanced AI-driven applications.