This review explores the application of chain-of-thought prompting in large language models (LLMs) within the field of nephrology. Chain-of-thought prompting enhances LLMs by encouraging them to explain their reasoning process, thereby improving their precision, contextual understanding, and alignment with human decision-making. This approach is particularly beneficial in medicine, where handling complex information, logical reasoning, and ethical considerations are crucial. The review highlights the potential of chain-of-thought prompting to improve medical care and research by bridging the gap between AI's opaque decision-making and the clear, accountable standards required in healthcare. LLMs, such as GPT-4, are powerful tools that can process vast amounts of data and provide actionable insights. They are used in various fields, including medicine, where they assist in diagnostics, treatment planning, and research. In nephrology, LLMs are being integrated to address complex challenges in kidney disease diagnosis and management. Chain-of-thought prompting is especially valuable in this context, as it enables LLMs to break down complex tasks into sequential steps, leading to more accurate and contextually relevant responses. The review discusses different prompting methods, including zero-shot, few-shot, and chain-of-thought prompting, and their effectiveness in various applications. Chain-of-thought prompting is shown to provide more consistent, detailed, and transparent responses, particularly in complex medical scenarios. It enhances diagnostic accuracy, contextual understanding, and collaborative decision-making, making it a valuable tool in healthcare. Examples of chain-of-thought prompts in nephrology are provided, demonstrating how this approach can be used to diagnose conditions such as hyponatremia due to SIADH, metabolic acidosis due to ethylene glycol intoxication, and hypertension due to fibromuscular dysplasia of the renal artery. These examples illustrate the benefits of chain-of-thought prompting in providing a structured, logical, and evidence-based approach to medical diagnosis. The review also discusses the future implications of chain-of-thought prompting in personalized treatment plans for kidney disease, emphasizing its potential to improve risk assessment, real-time treatment adaptation, medication optimization, and integration with existing clinical systems. The integration of LLMs with electronic health records (EHRs) is highlighted as a key step in enhancing clinical decision-making and patient care. However, the review also notes the importance of ensuring patient safety, privacy, and the need for thorough testing and validation before widespread implementation. Overall, the review underscores the transformative potential of chain-of-thought prompting in advancing nephrology and healthcare in general.This review explores the application of chain-of-thought prompting in large language models (LLMs) within the field of nephrology. Chain-of-thought prompting enhances LLMs by encouraging them to explain their reasoning process, thereby improving their precision, contextual understanding, and alignment with human decision-making. This approach is particularly beneficial in medicine, where handling complex information, logical reasoning, and ethical considerations are crucial. The review highlights the potential of chain-of-thought prompting to improve medical care and research by bridging the gap between AI's opaque decision-making and the clear, accountable standards required in healthcare. LLMs, such as GPT-4, are powerful tools that can process vast amounts of data and provide actionable insights. They are used in various fields, including medicine, where they assist in diagnostics, treatment planning, and research. In nephrology, LLMs are being integrated to address complex challenges in kidney disease diagnosis and management. Chain-of-thought prompting is especially valuable in this context, as it enables LLMs to break down complex tasks into sequential steps, leading to more accurate and contextually relevant responses. The review discusses different prompting methods, including zero-shot, few-shot, and chain-of-thought prompting, and their effectiveness in various applications. Chain-of-thought prompting is shown to provide more consistent, detailed, and transparent responses, particularly in complex medical scenarios. It enhances diagnostic accuracy, contextual understanding, and collaborative decision-making, making it a valuable tool in healthcare. Examples of chain-of-thought prompts in nephrology are provided, demonstrating how this approach can be used to diagnose conditions such as hyponatremia due to SIADH, metabolic acidosis due to ethylene glycol intoxication, and hypertension due to fibromuscular dysplasia of the renal artery. These examples illustrate the benefits of chain-of-thought prompting in providing a structured, logical, and evidence-based approach to medical diagnosis. The review also discusses the future implications of chain-of-thought prompting in personalized treatment plans for kidney disease, emphasizing its potential to improve risk assessment, real-time treatment adaptation, medication optimization, and integration with existing clinical systems. The integration of LLMs with electronic health records (EHRs) is highlighted as a key step in enhancing clinical decision-making and patient care. However, the review also notes the importance of ensuring patient safety, privacy, and the need for thorough testing and validation before widespread implementation. Overall, the review underscores the transformative potential of chain-of-thought prompting in advancing nephrology and healthcare in general.