Chain of Thought

Chain of thought (CoT) is a technique in which a language model is encouraged to produce intermediate reasoning steps before stating a final answer. It can be triggered by asking the model to think step by step, by showing worked examples, or by using models trained to reason before responding.

Why it matters

Models often fail on arithmetic, logic and multi-step planning when they must jump straight to an answer. Spelling out steps gives the model more computation per problem and makes errors easier for a person to spot. The cost is more output tokens, higher latency and higher price. For simple lookups or classification it adds little.

Example

Q: A server handles 120 requests per minute. How many in 2.5 hours?
Reasoning: 2.5 hours = 150 minutes.
120 x 150 = 18,000.
Answer: 18,000 requests.

Asking for the answer alone makes slips in the multiplication more likely.

Common misconceptions

• "The written reasoning is always the real cause of the answer." It can be a plausible story that does not faithfully reflect how the model arrived there.
• "More steps always means better accuracy." Long chains can compound an early mistake.
• It does not remove hallucination; check facts independently.

Related

• Prompt engineering
• Few-shot prompting
• Hallucination
• LLM

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