Chain-of-Thought
Chain-of-Thought is a technique where the model reasons step by step before answering. On problems with counting and logic, accuracy is noticeably higher.
Chain-of-Thought is when the model does not immediately give an answer, but first pronounces the reasoning step by step. On tasks where there is logic or arithmetic (calculate unit economics, expand a funnel), step-by-step thinking sharply reduces the number of errors.
In modern 2026 models this is often built in - “thinking” modes. But the technique also works in a prompt: a phrase like “reason step by step, then give an answer” turns it on manually.
For marketing, it is useful where AI calculates: media plan, CPL forecast, analysis of metrics. If you ask the model to immediately give out a number, it can “guess”. If you ask to show a calculation, both you and she can see the error.
Related terms
Where is it understood in practice?
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