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Prompting a model to reason through intermediate steps before giving a final answer, which improves accuracy on multi-step problems.
Chain-of-thought prompting asks the model to show its working — laying out intermediate reasoning steps — before stating an answer. Wei et al. (2022) found that this simple change substantially improves performance on arithmetic, commonsense and symbolic-reasoning benchmarks, and that the effect strengthens with model scale.
The trade-off is speed and token cost: more reasoning means longer outputs. It helps most on problems that genuinely require several steps.
Primary, peer-reviewed and archival sources for this definition.
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