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Chain-of-thought prompting elicits step-by-step reasoning from language models. The article explains how revealing intermediate steps can boost accuracy on complex math, logic, and planning tasks, surveys prompting tactics (few-shot exemplars, rationale cues, self-consistency), weighs trade-offs (verbosity, latency, leakage), and offers tips for evaluation, trimming, and safe deployment.