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Résumé : Retrieval-augmented generation (RAG) is one of the best methods for performing language tasks these days. RAG uses large language models with external tools for better accuracy, correctness, and contextual understanding. The review discusses key concepts in RAG, how RAG differs from previous systems that retrieved or generated content, how RAG is utilized in question answering, knowledge management, and content generation, and it discusses some limitations, including quality of retrieval, requirement for high-performance computers, complicated integration, as well as data privacy problems, and presents new approaches to address these limitations. The review examines over a quarter-century of key recent studies and concludes with some future research areas, such as multimodal RAG, novel methods for retrieval, personalization, ethics, explanability, as well as incorporating human feedback. The review seeks to make the reader understand RAG's current state as well as future research areas through intensive discussion as well as lucid examples.