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Résumé : This literature review examines the use of Retrieval-Augmented Generation (RAG) in enhancing Large Language Models (LLM) for domain-specific knowledge. RAG integrates retrieval techniques with generative models to access external knowledge sources, addressing the limitations of LLMs in handling specialized information. By leveraging external data, RAG improves the accuracy and relevance of generated content, making it particularly useful in fields that require detailed and up-to-date knowledge. This review highlights the effectiveness of RAG in overcoming challenges such as data sparsity and the dynamic nature of specialized knowledge. Furthermore, it discusses the potential of RAG to enhance LLM performance, scalability, and the ability to generate contextually accurate responses in knowledge-intensive applications. Key challenges and future research directions in the implementation of RAG for domain-specific knowledge are also identified.