RAG (Retrieve, Answer, Generate) is a framework and model for open-domain question answering (QA) that combines information retrieval, document ranking, and natural language generation techniques to generate high-quality answers to user queries. It uses a retriever component to retrieve relevant documents, a reader component to extract answers from retrieved documents, and a generator component to generate natural language responses. Readers can explore RAG's architecture, training methodology, and applications in various QA tasks, such as factoid QA, passage retrieval, and document summarization, understanding its potential to improve QA systems' performance and usability.