Chain-of-Thought

AI concepts
About 1 min read

A prompting and reasoning technique that improves problem-solving accuracy for complex questions by prompting the AI to go through intermediate logical reasoning steps before outputting the final answer.

Also known as
CoTChain-of-ThoughtChain-of-Thought Prompting

Detailed explanation

Chain-of-Thought (CoT) is a technique that enables Large Language Models (LLMs) to decompose and process problems step-by-step, akin to human thinking. It shows vastly superior performance in mathematical calculations, common-sense reasoning, and complex decision-making compared to simple Q&A approaches. Moving beyond early instruction-based methods like 'let's think step-by-step' (Zero-shot CoT), it has recently evolved into the core mechanism of 'reasoning models'—such as OpenAI o1—which internalize the reasoning process during model training to solve complex tasks. This helps the model review intermediate steps, reducing logical leaps and improving accuracy.

Why it matters in tool selection

If your goal is designing business logic, writing complex code, or analyzing data rather than simple information summarization, you should select a model with proven CoT performance. In particular, recent reasoning-focused models internalize CoT to run through the thought process on their own without users writing separate prompts, which becomes a key factor in determining the reliability of workflow automation.

What to check

  • Does the model present its logical rationale alongside the answer?
  • Is the accuracy stable in complex formulas or multi-step logic problems?
  • Does the model expose its reasoning process (CoT), or does it process it internally and only deliver the result?
  • Can you afford the additional token costs and response latency generated when CoT is activated?

Example

Question: 'Chulsoo had 5 apples, gave 2 to Younghee, and bought 4 more. How many apples does he have now?' Standard Answer: '7.' CoT Applied Answer: '1. Initial apples: 5 / 2. After giving to Younghee: 5-2 = 3 / 3. After buying more: 3+4 = 7 / Final Answer: 7'

Confusing terms

Standard Prompting

A method of immediately demanding only the final answer to a question without intermediate steps.

Self-Consistency

A complementary technique that generates multiple reasoning paths via CoT and selects the answer that appears most frequently.

Related terms

Reasoning ModelPrompt Engineering