Retrieval augmented generation (RAG) is a technique that combines retrieval and generation models to improve the performance of natural language processing (NLP) tasks. RAG models work by first retrieving relevant information from a large knowledge base, and then using that information to generate a response. This approach can lead to more accurate and informative responses than traditional generation models, which are limited by their own knowledge and understanding.