Chain-of-thought prompting asks a model to work through intermediate reasoning before giving an answer. It can help with tasks that contain several dependent steps, such as arithmetic, planning, or logic.
Writing intermediate steps gives the model more computation and can make mistakes easier to spot. However, a detailed rationale is not proof that the answer is correct. Models can invent convincing explanations after making a guess.
For modern reasoning models, follow the provider's guidance because explicit “think step by step” prompts may add little or reduce performance. In user-facing systems, request a concise explanation or verifiable work rather than hidden internal reasoning. Check results with tools, tests, or source evidence whenever possible.
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