Chain of Thought

Also known as: CoT Prompting, Step-by-Step Reasoning

A prompting technique that guides AI models to break down complex problems into intermediate reasoning steps.

What is Chain of Thought?

Chain of Thought (CoT) is an advanced prompting technique that encourages large language models to generate a series of intermediate reasoning steps before arriving at a final answer. By explicitly asking the model to 'think step by step' or by demonstrating the process through examples, CoT enables more transparent, logical problem-solving and significantly improves performance on complex tasks requiring multi-step reasoning.

Why It Matters

Chain of Thought is vital for AI optimization because it dramatically improves model performance on complex reasoning tasks by up to 30-40% in some cases. It reduces errors by making the reasoning process explicit, enables humans to verify the model's logic, and helps identify where reasoning breaks down. This technique is particularly valuable for applications requiring reliable decision-making or problem-solving capabilities.

Use Cases

Mathematical Problem Solving

Breaking down complex calculations into sequential steps for accurate solutions.

Logical Reasoning

Working through arguments or scenarios step-by-step to reach valid conclusions.

Complex Decision Making

Evaluating multiple factors and their relationships before making recommendations.

Optimization Techniques

To optimize Chain of Thought prompting, include explicit instructions like 'Let's think about this step by step' or provide examples of step-by-step reasoning for similar problems. For complex tasks, consider combining CoT with other techniques like self-consistency (generating multiple reasoning paths and taking the majority answer).

Metrics

Measure CoT effectiveness through accuracy on reasoning tasks, logical consistency between steps, error reduction rates compared to direct prompting, and human evaluation of reasoning quality. The presence of clear intermediate steps that lead logically to the conclusion is a key quality indicator.

LLM Interpretation

LLMs process Chain of Thought prompts by following the demonstrated reasoning pattern. The explicit steps activate the model's ability to decompose problems and maintain logical consistency throughout the solution process. This approach leverages the model's learned patterns of logical reasoning while providing structure that constrains potential errors.

Code Example

// Example of Chain of Thought prompting
const cotPrompt = `
Problem: A store sells notebooks for $4 each and pens for $1 each. 
If I buy 3 notebooks and twice as many pens, how much do I spend in total?

Let's think step by step:
1. Cost of notebooks: 3 notebooks × $4 = $12
2. Number of pens: 3 notebooks × 2 = 6 pens
3. Cost of pens: 6 pens × $1 = $6
4. Total cost: $12 + $6 = $18

Therefore, I spend $18 in total.

Problem: A shirt costs $25. During a sale, the price is reduced by 20%. 
How much does the shirt cost during the sale?

Let's think step by step:
`;

const response = await llm.generate(cotPrompt);

Structured Data

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  "@type": "DefinedTerm",
  "name": "Chain of Thought",
  "alternateName": [
    "CoT Prompting",
    "Step-by-Step Reasoning"
  ],
  "description": "A prompting technique that guides AI models to break down complex problems into intermediate reasoning steps.",
  "inDefinedTermSet": {
    "@type": "DefinedTermSet",
    "name": "AI Optimization Glossary",
    "url": "https://geordy.ai/glossary"
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}

Term Details

Category: AI Techniques
Type: technique
Expertise Level: strategist
GEO Readiness: structured