What is Retrieval Augmented Generation?

Retrieval Augmented Generation (RAG) is a combination of retrieval-based and generation-based methods in natural language processing, often used in conversational agents, question answering systems, and content creation. RAG involves using a retrieval model to fetch relevant information from a knowledge base and a generation model to refine or expand upon this information for more contextually appropriate responses. This hybrid approach aims to leverage the strengths of both methods while addressing their limitations. Key benefits of RAG include factual accuracy, context awareness, flexibility, adaptability to various tasks, efficient use of resources, and improved user experience.