检索增强生成（RAG）

Retrieval-Augmented Generation (RAG) is a technology that combines information retrieval with text generation to enhance the accuracy and relevance of generative models. By incorporating external knowledge bases during the generation process, RAG effectively addresses knowledge gaps in generative models, resulting in richer and more accurate text outputs. This technology shows significant potential for applications in natural language processing, particularly in tasks like question answering and dialogue generation. The core of RAG lies in augmenting the generation process with retrieved relevant information, allowing the generated content to reflect not only the training data of the model but also the latest information and knowledge. Successful implementation of this method relies on the integration of efficient information retrieval mechanisms with robust generative models.