With the continuous advancement of deep learning and Transformer architectures, natural language processing tasks based on large language models have achieved remarkable progress. However, issues such as hallucinations, information lag, and lack of domain-specific knowledge persist. To address these issues, retrieval-augmented generation techniques introduce retrieval mechanisms based on external knowledge repositories, enabling models not only to use existing knowledge but also to retrieve external data in real time, thus improving the accuracy, timeliness, and adaptability of generated content. This study examines how retrieval-augmented generation enhances the generative capabilities of large language models. First, from the perspectives of retrieval augmentation and augmented generation, it systematically classifies retrieval methods according to different retrieval objectives and multiple technical pathways for augmented generation. Subsequently, the principles, implementation methods, and applications of retrieval-augmented generation technologies across various domains are analyzed and summarized. Finally, the current applications, challenges, and future prospects of retrieval-augmented generation across multiple fields are examined.