This review explores the integration of retrieval-augmented generation (RAG) with large language models (LLMs) in nephrology to enhance practical applications. The authors highlight the potential of LLMs in healthcare, particularly in nephrology, for improving patient care, medical research, and education. However, challenges such as hallucinations and inaccuracies in LLM outputs remain. To address these, RAG is proposed as a strategy that integrates external data to improve accuracy and relevance. The review presents a case study of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease (CKD). This model demonstrates the potential for providing accurate, specialized medical advice, advancing reliable and efficient nephrology practices. The review also discusses the application of RAG in nephrology, including integrating the latest research and guidelines, case-based learning, and a multidisciplinary approach. It outlines the process of creating a CKD-specific knowledge base for RAG, emphasizing the importance of accurate, up-to-date information. The review concludes that RAG enhances the effectiveness of LLMs in nephrology, offering a more reliable and precise tool for clinical decision-making and patient education. Future studies are suggested to further explore the potential of RAG in nephrology, including its integration with electronic health records and other medical domains. The review emphasizes the need for ongoing research, ethical considerations, and collaboration between AI experts, nephrologists, and ethicists to ensure the responsible and effective use of AI in healthcare.This review explores the integration of retrieval-augmented generation (RAG) with large language models (LLMs) in nephrology to enhance practical applications. The authors highlight the potential of LLMs in healthcare, particularly in nephrology, for improving patient care, medical research, and education. However, challenges such as hallucinations and inaccuracies in LLM outputs remain. To address these, RAG is proposed as a strategy that integrates external data to improve accuracy and relevance. The review presents a case study of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease (CKD). This model demonstrates the potential for providing accurate, specialized medical advice, advancing reliable and efficient nephrology practices. The review also discusses the application of RAG in nephrology, including integrating the latest research and guidelines, case-based learning, and a multidisciplinary approach. It outlines the process of creating a CKD-specific knowledge base for RAG, emphasizing the importance of accurate, up-to-date information. The review concludes that RAG enhances the effectiveness of LLMs in nephrology, offering a more reliable and precise tool for clinical decision-making and patient education. Future studies are suggested to further explore the potential of RAG in nephrology, including its integration with electronic health records and other medical domains. The review emphasizes the need for ongoing research, ethical considerations, and collaboration between AI experts, nephrologists, and ethicists to ensure the responsible and effective use of AI in healthcare.