Retrieval-Augmented Generation (RAG) is an advanced architecture designed to enhance large language models (LLMs) by providing them with relevant and context-driven data, addressing their limitations in accessing up-to-date and domain-specific knowledge. The RAG system comprises embedding models to convert data into vectors, a vector database for storing and retrieving these embeddings, and a large language model that utilizes the context from the database to generate answers. Clarifai offers a comprehensive platform combining these components, allowing users to create RAG applications efficiently. The process involves setting up the system using Clarifai's Python SDK, uploading documents to the vector database, and interacting with the data through a chat method, enabling users to summarize and query their documents. The setup is simplified to four lines of code, with flexibility in choosing the language models and workflows, making it accessible for various use cases.