Abstract: Retrieval-Augmented Generation (RAG), as a paradigm that integrates information retrieval and text generation, significantly enhances the knowledge coverage, factual accuracy, and timeliness of models by incorporating external knowledge sources. This paper systematically reviews the technical evolution and research status of RAG, starting from its basic principles and system architecture. It delves into the co-modeling mechanisms of the retriever and generator and their typical variants, including FiD, REALM, REPLUG, and GraphRAG. Furthermore, it summarizes the practical effects of RAG in application scenarios such as open-domain question answering, domain knowledge question answering, intelligent customer service, and enterprise knowledge management, and explores cutting-edge directions such as multimodal fusion, external tool invocation, and few-shot learning. Based on this, the paper points out the challenges currently faced by RAG research, such as the lack of a unified evaluation system, high training costs, and insufficient controllability, and looks forward to future development trends. This paper aims to provide a systematic theoretical framework and practical reference for subsequent research.