Chain of Thought

Break down complex problems into sequential steps for clearer reasoning.

Chain of Thought (CoT) prompting is a powerful technique that encourages AI models to show their reasoning process by breaking down complex problems into sequential, logical steps. This approach significantly improves the model's ability to handle multi-step reasoning tasks and provides transparency into how conclusions are reached.

Key Features

Step-by-step problem decomposition
Transparent reasoning process
Improved accuracy on complex tasks
Easy to follow and verify logic
Works well with mathematical and logical problems

Pros and Cons

Advantages

Increases accuracy on complex reasoning tasks
Provides transparent thought process
Helps identify logical errors
Improves understanding of AI decisions
Works well across different domains

Considerations

Longer responses take more time and tokens
May overcomplicate simple problems
Requires clear step definition
Can lead to verbose outputs
May not always be necessary for straightforward tasks

Framework Template

Let's approach "[Your prompt content]" step by step:

1. **Understanding the request**: Break down what you're asking for
2. **Analyzing the context**: Consider the relevant factors and constraints  
3. **Developing the response**: Structure a comprehensive answer
4. **Validation**: Ensure the response addresses your specific needs

Please walk me through your reasoning process for each step.

Example: Mathematical Problem Solving

Prompt

Solve this step by step: If a store sells 150 items per day and makes $20 profit per item, how much profit will they make in a month (30 days)?

Expected Response

Let me solve this step by step: 1. **Daily profit calculation**: 150 items × $20 profit = $3,000 per day 2. **Monthly calculation**: $3,000 × 30 days = $90,000 3. **Verification**: 150 × 20 × 30 = 90,000 ✓ The store will make $90,000 profit in a month.