PromptSharp › Glossary › Chain-of-Thought Prompting
What is Chain-of-Thought Prompting?
Chain-of-thought prompting asks the model to reason step by step before answering, improving accuracy on multi-step problems.
Chain-of-thought (CoT) prompting instructs the model to work through a problem in intermediate steps before giving a final answer. On math, logic, and multi-step analysis, laying out the reasoning first meaningfully reduces errors versus jumping straight to an answer.
The classic trigger is a phrase like 'Let's think step by step,' but the stronger version specifies the steps you want: 'First list the assumptions, then compute each figure, then state the conclusion.' Some newer 'reasoning' models do this internally, but explicit CoT still helps for auditability — you can see and check the work.
Example
A store sells pens at 3 for $2. How much for 12 pens? Think step by step, then give the final price.
Frequently asked
Does chain-of-thought always improve results?
It helps most on multi-step reasoning, math, and analysis. For simple lookups or single-step rewrites it adds length without benefit. Use it when the task has intermediate steps that can go wrong.
What is the difference between chain-of-thought and prompt chaining?
Chain-of-thought happens inside ONE response — the model reasons then answers. Prompt chaining splits work across MULTIPLE prompts, feeding each output into the next.
Related terms
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