The article discusses the integration of large language models (LLMs) with retrieval-augmented generation (RAG) in nephrology, highlighting its potential to enhance patient care, medical research, and education. LLMs, such as ChatGPT, have advanced from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data. However, they face challenges like hallucinations and imperfect accuracy, which are critical issues in healthcare due to the need for precision. To address these challenges, strategies like prompt engineering and RAG have been developed. RAG integrates external data to enhance output accuracy and relevance, making it particularly useful for tasks requiring up-to-date, comprehensive information. The authors showcase the creation of a specialized ChatGPT model integrated with an RAG system tailored to align with the KDIGO 2023 guidelines for chronic kidney disease (CKD). This model provides specialized, accurate medical advice, demonstrating its potential in improving nephrology practices. The article also explores the strengths and weaknesses of RAG, emphasizing its ability to access current, validated information and its limitations in data quality and retrieval errors. The integration of LLMs with RAG in nephrology is expected to enhance the depth and breadth of research, clinical decision support, patient education, and personalized medicine. Future studies are suggested to evaluate the effectiveness of these models in real-world clinical settings, integrate them with electronic health records, and explore their potential in other medical domains. The authors conclude that combining LLMs with RAG systems in nephrology represents a significant advancement, but ongoing research and ethical considerations are essential to ensure accuracy, innovation, and patient welfare.The article discusses the integration of large language models (LLMs) with retrieval-augmented generation (RAG) in nephrology, highlighting its potential to enhance patient care, medical research, and education. LLMs, such as ChatGPT, have advanced from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data. However, they face challenges like hallucinations and imperfect accuracy, which are critical issues in healthcare due to the need for precision. To address these challenges, strategies like prompt engineering and RAG have been developed. RAG integrates external data to enhance output accuracy and relevance, making it particularly useful for tasks requiring up-to-date, comprehensive information. The authors showcase the creation of a specialized ChatGPT model integrated with an RAG system tailored to align with the KDIGO 2023 guidelines for chronic kidney disease (CKD). This model provides specialized, accurate medical advice, demonstrating its potential in improving nephrology practices. The article also explores the strengths and weaknesses of RAG, emphasizing its ability to access current, validated information and its limitations in data quality and retrieval errors. The integration of LLMs with RAG in nephrology is expected to enhance the depth and breadth of research, clinical decision support, patient education, and personalized medicine. Future studies are suggested to evaluate the effectiveness of these models in real-world clinical settings, integrate them with electronic health records, and explore their potential in other medical domains. The authors conclude that combining LLMs with RAG systems in nephrology represents a significant advancement, but ongoing research and ethical considerations are essential to ensure accuracy, innovation, and patient welfare.