Chain-of-thought prompting enhances the capabilities of large language models (LLMs) by making them more specific, context-aware, and aligned with human thinking and decision-making processes. This approach is particularly beneficial in nephrology, where complex information handling, logical reasoning, and ethical considerations are crucial. The review focuses on how chain-of-thought prompting can be adapted for nephrology, examining its technical aspects, ethical implications, and future possibilities. Key benefits include enhanced diagnostic accuracy, contextual understanding, collaborative decision-making, multistep problem-solving, efficiency, data annotation, personalization, and research applications. The review also highlights the importance of understanding AI decisions through chain-of-thought prompting, making them more transparent and explainable. Examples of chain-of-thought prompts in nephrology, such as diagnosing hyponatremia due to SIADH, metabolic acidosis due to ethylene glycol intoxication, and hypertension due to fibromuscular dysplasia of the renal artery, demonstrate the method's effectiveness. Future implications include individualized risk assessment, real-time treatment adaptation, medication optimization, integration with clinical systems, and continuous learning. Ethical considerations, including compliance with healthcare regulations and standards, are also discussed, emphasizing the need for ethical protocols to ensure the safe and responsible use of AI in healthcare. Overall, chain-of-thought prompting holds significant promise for improving patient care and medical research in nephrology.Chain-of-thought prompting enhances the capabilities of large language models (LLMs) by making them more specific, context-aware, and aligned with human thinking and decision-making processes. This approach is particularly beneficial in nephrology, where complex information handling, logical reasoning, and ethical considerations are crucial. The review focuses on how chain-of-thought prompting can be adapted for nephrology, examining its technical aspects, ethical implications, and future possibilities. Key benefits include enhanced diagnostic accuracy, contextual understanding, collaborative decision-making, multistep problem-solving, efficiency, data annotation, personalization, and research applications. The review also highlights the importance of understanding AI decisions through chain-of-thought prompting, making them more transparent and explainable. Examples of chain-of-thought prompts in nephrology, such as diagnosing hyponatremia due to SIADH, metabolic acidosis due to ethylene glycol intoxication, and hypertension due to fibromuscular dysplasia of the renal artery, demonstrate the method's effectiveness. Future implications include individualized risk assessment, real-time treatment adaptation, medication optimization, integration with clinical systems, and continuous learning. Ethical considerations, including compliance with healthcare regulations and standards, are also discussed, emphasizing the need for ethical protocols to ensure the safe and responsible use of AI in healthcare. Overall, chain-of-thought prompting holds significant promise for improving patient care and medical research in nephrology.