Chain-of-Thought

Building a Thinking Model from a Non-Thinking Model with Chain-of-Thought

Most AI models answer directly without showing their reasoning. While fine for simple tasks, this “non-thinking” approach often fails for problems requiring multi-step logic. Chain-of-Thought (CoT) prompting solves this by guiding the model to explain its steps before giving the final answer.

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What is Chain-of-Thought?

Chain-of-Thought prompting tells the model to “think aloud,” breaking problems into clear steps.
Example:
Prompt: “If a train travels 60 km in 1 hour, how far in 4 hours? Show steps.”
Answer:

1. Distance/hour = 60 km

2. 60 × 4 = 240 km
   Final: 240 km

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How to Turn a Non-Thinking Model into a Thinking Model

1. Explicit Instructions – Use phrases like “Explain step-by-step” or “Show reasoning.”

2. Few-Shot Examples – Provide worked examples so the model learns the format.

3. Self-Check – Ask the model to verify its result.

4. Stage Reasoning – Solve in stages for complex problems.

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Benefits

• More accurate for multi-step tasks

• Transparent reasoning (easy to spot errors)

• Works across math, logic, coding, planning

Note: CoT increases response length and may “overthink” simple tasks, so use it where reasoning matters.