Chain-of-Thought (CoT) prompting encourages the AI to think step by step before giving the final answer. This leads to better reasoning, fewer mistakes and deeper understanding of complex problems.
In simple terms
Ask the AI to show its reasoning in steps before answering, and it gets complex problems right more often.
How it works
- 1You give a complex question that needs multiple steps of reasoning.
- 2The AI thinks step by step, breaking the problem into intermediate steps.
- 3It follows the reasoning chain to connect the steps.
- 4It provides the final answer based on the reasoning.
- 5The result is more accurate and reliable, and easier to verify.
Key points
- Improves accuracy on math, logic and multi-step problems.
- Encourages logical, step-by-step thinking.
- Makes answers easier to verify and trust.
- Trigger it with phrases like 'think step by step' or 'let's reason it out'.
Why it matters
Chain-of-thought is one of the simplest ways to boost reasoning quality. By making the model reason explicitly, you catch and reduce errors on problems that one-shot answers get wrong.
Frequently asked questions
- How do I trigger chain-of-thought?
- Add a phrase like 'think step by step' or ask the model to explain its reasoning before answering.
- Does it always help?
- Most on multi-step reasoning, math and logic; for simple lookups it adds little and uses more tokens.
More in Prompting
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Few-Shot Prompting
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Zero-Shot Prompting
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