Chain-of-Thought (CoT) prompting has become one of the most powerful techniques for getting AI systems to produce high-quality, well-reasoned responses. Learn how to leverage this technique.
What is Chain-of-Thought Prompting?
CoT prompting asks AI systems to show their work—to explain their reasoning step-by-step before reaching a conclusion. This simple technique dramatically improves accuracy and reliability.
The Science
By prompting the model to think through problems explicitly, we leverage the model’s ability to break down complex tasks. The intermediate steps improve final answer quality.
Practical Example
Weak Prompt: “Is 15 × 3 equal to 40?”
CoT Prompt: “Let’s think step-by-step. First, multiply 15 by 3. What is 15 × 3? Then, compare your result to 40. Is it equal?”
When to Use CoT
- Mathematical and logical problems
- Multi-step tasks
- Analysis and interpretation
- Decision-making scenarios
- Complex reasoning requirements
Pro Tips
Include explicit language like “Let’s think step-by-step” or “Break this down into steps.” Provide examples of good step-by-step reasoning for few-shot learning.
Tags: chain-of-thought, reasoning prompts, AI logic, problem-solving