Chain-of-thought reasoning is a technique in which a language model generates a series of intermediate steps before arriving at a final answer. This process emulates human reasoning by breaking down complex problems into manageable, sequential steps, which improves the model’s accuracy and transparency. By using chain-of-thought, models are better able to handle multi-step problems and provide explanations that reveal how they reached a conclusion, thereby reducing errors and increasing interpretability.