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Schedule a callChain-of-Thought Prompting
Prompting a model to reason step by step before it answers, which improves accuracy on multi-step problems.
Chain-of-thought prompting asks a model to show its intermediate reasoning rather than jumping to a conclusion. On problems that require several dependent steps — arithmetic, logic, multi-constraint planning — generating the steps measurably improves accuracy, because each step conditions the next instead of the whole answer resting on a single leap. It is invoked either by instruction or by including worked examples in the prompt. The trade-off is cost and latency: reasoning tokens are billed and generated like any others, so chain-of-thought earns its place on hard problems and wastes money on simple lookups.
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