Retrieval-Augmented Generation (RAG) is an AI framework designed to enhance large language models (LLMs) like GPT-3 and GPT-4 by grounding them in accurate, up-to-date external knowledge bases, thus overcoming their limitations of relying on outdated or incorrect data. This process involves organizing data into smaller chunks, converting them into vectors, and using these vectors to retrieve relevant information, which is then provided as context to the LLMs to generate informed and precise responses. The tutorial demonstrates how to implement RAG using libraries such as Streamlit, Langchain, and Clarifai, allowing developers to build systems that leverage LLMs while improving the reliability and transparency of their outputs. Through a multi-step procedure involving data organization, vector creation, and the setup of a Q&A model, RAG facilitates the generation of responses that are not only convincing but also verifiable, thereby enhancing trust in AI applications.