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Retrieval-Augmented Generation
RAG is a natural language processing approach that combines information retrieval and text generation techniques. It entails using a pre-trained language model to generate responses based on retrieved data, allowing the model to access external knowledge sources during the generation process, thereby improving the quality and relevance of the generated content.