Retrieval-augmented generation (RAG) is an advanced natural language processing approach that combines retrieval and generation techniques to produce more accurate and contextually relevant text. In RAG, a retrieval system first searches a large corpus of documents to find relevant information based on a given query. Then, a generative model uses this retrieved information to construct a coherent and contextually appropriate response. This method enhances the quality of generated text by grounding it in actual data, making it particularly useful for tasks requiring detailed and precise information.