One of the most powerful prompt engineering techniques is chain-of-thought prompting. Instead of asking for a direct answer, you prompt the model to work through its reasoning step by step. This simple change can dramatically improve accuracy on complex problems—sometimes doubling or tripling success rates. Here's how to use it effectively.
What Is Chain-of-Thought Prompting?
Chain-of-thought prompting asks models to show their work, just like you'd ask a student to show their math steps. Instead of jumping to conclusions, the model breaks down problems into intermediate steps, making each reasoning move explicit. This process helps the model avoid errors and produces more reliable results.
Research shows this technique improves performance on arithmetic, logical reasoning, symbolic manipulation, and other multi-step problems. The improvement is often substantial—models that fail on complex problems when asked directly can succeed when prompted to think step by step.
Basic Technique
The simplest chain-of-thought prompt adds "Let's think step by step" or "Show your reasoning" to your query. This often triggers the model to break down its thinking:
Example:
❌ Without chain-of-thought:
If a train travels 60 mph for 2.5 hours, then 45 mph for 1.5 hours, what's the average speed?
✓ With chain-of-thought:
If a train travels 60 mph for 2.5 hours, then 45 mph for 1.5 hours, what's the average speed? Let's think step by step.
Advanced Patterns
Few-Shot Chain-of-Thought
Show the model examples of step-by-step reasoning. This teaches it the format and approach you want:
Q: A store has 15 apples. They sell 6 and get 12 more. How many do they have? A: Let's think step by step. Start: 15 apples Sell 6: 15 - 6 = 9 apples Get 12 more: 9 + 12 = 21 apples Answer: 21 apples Q: [Your problem here] A: Let's think step by step.
Structured Reasoning
For complex problems, structure the reasoning explicitly:
Analyze this problem step by step: 1. Identify what we're trying to find 2. List the information we have 3. Determine what approach to use 4. Work through the solution 5. Verify the answer makes sense [Your problem]
When to Use Chain-of-Thought
Chain-of-thought is most valuable for:
- Mathematical problems: Multi-step calculations, word problems
- Logical reasoning: Deductive reasoning, puzzle solving
- Analysis tasks: Breaking down complex questions
- Planning: Multi-step processes, decision trees
- Debugging: Understanding why code or logic fails
For simple factual questions or creative tasks, chain-of-thought may be unnecessary and can slow responses. Use it when problems require multiple reasoning steps.
Common Mistakes
Too vague: Just saying "think about it" isn't enough. Be explicit about showing steps.
Over-structuring: For simple problems, excessive structure can confuse the model. Match the structure to the problem's complexity.
Ignoring the output: The reasoning steps are valuable—they let you catch errors and understand the model's thinking. Don't just skip to the final answer.
Real-World Applications
I use chain-of-thought prompting for code debugging, data analysis, and complex problem-solving. When a model gives an incorrect answer, asking it to show its reasoning often reveals where it went wrong. You can then correct the reasoning and get better results.
For business analysis, chain-of-thought helps models break down complex questions into manageable parts. Instead of asking "Should we expand to Europe?", ask "Let's analyze European expansion step by step: market size, competition, costs, risks, and timeline." The structured approach produces more thorough analysis.
Key Takeaways
- • Chain-of-thought dramatically improves accuracy on complex problems
- • Simple addition of "think step by step" often works
- • Few-shot examples teach the desired reasoning format
- • Most valuable for multi-step problems
- • The reasoning process is as valuable as the answer