Chain-of-thought prompting is a technique designed to enhance the performance of AI models, particularly large language models like GPT-4, by guiding them through a step-by-step reasoning process to arrive at a conclusion or answer. Unlike few-shot prompting, which focuses on providing examples of the desired output, chain-of-thought prompting encourages the model to “think aloud” by breaking down complex problems into intermediate steps. This method is especially useful for tasks requiring logical reasoning, problem-solving, or when an explanation of the thought process is as important as the final answer.