Chain-of-Thought Prompting: Make AI Think Step by Step
By Learnia Team
Chain-of-Thought Prompting: Make AI Think Step by Step
This article is written in English. Our training modules are available in French.
Have you ever asked an AI a complex question only to receive a wrong or shallow answer? The solution might be simpler than you think: ask the AI to think step by step.
Chain-of-Thought (CoT) prompting is one of the most powerful techniques in modern prompt engineering. It transforms how Large Language Models reason through problems—and the results are remarkable.
What Is Chain-of-Thought Prompting?
Chain-of-Thought prompting is a technique that encourages AI models to break down complex problems into intermediate reasoning steps before arriving at a final answer.
Instead of jumping directly to a conclusion, the model "thinks aloud," showing its work just like a student solving a math problem on a whiteboard.
Standard Prompting vs. Chain-of-Thought
Standard prompting:
Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
A: 11
Chain-of-Thought prompting:
Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
A: Roger started with 5 balls. He bought 2 cans × 3 balls = 6 balls. So he now has 5 + 6 = 11 tennis balls.
The answer is the same, but the reasoning is explicit—and for harder problems, this transparency dramatically improves accuracy.
Why Does Chain-of-Thought Work?
CoT prompting works because it mirrors how humans solve complex problems: by decomposing them into manageable steps.
Key Benefits
- →Improved accuracy on complex tasks: Research shows CoT can boost performance by 10-20% on reasoning benchmarks
- →Transparency: You can verify how the AI reached its conclusion
- →Error detection: When reasoning steps are visible, mistakes are easier to spot
- →Better performance on multi-step problems: Math, logic puzzles, and analytical tasks see the biggest gains
The Research Behind It
The landmark 2022 paper by Wei et al. at Google demonstrated that Chain-of-Thought prompting enabled PaLM 540B to improve from 55% to 74% accuracy on the GSM8K math benchmark—a 19-point jump simply by changing how the prompt was structured.
"Chain-of-thought prompting allows models to decompose multi-step problems into intermediate steps, solving problems that cannot be solved with standard prompting." — Wei et al., 2022
Zero-Shot Chain-of-Thought: The Magic Phrase
You don't always need elaborate examples. Sometimes, a simple phrase is enough.
Zero-Shot CoT involves adding a trigger phrase to your prompt:
"Let's think step by step."
That's it. This single sentence can unlock reasoning capabilities in the model without providing any examples.
Example
Without Zero-Shot CoT:
Is 17 a prime number? A: Yes.
With Zero-Shot CoT:
Is 17 a prime number? Let's think step by step.
A: To check if 17 is prime, I need to see if it's divisible by any number other than 1 and itself.
- →17 ÷ 2 = 8.5 (not divisible)
- →17 ÷ 3 = 5.67 (not divisible)
- →17 ÷ 4 = 4.25 (not divisible)
- →I only need to check up to √17 ≈ 4.1
Since 17 is not divisible by 2, 3, or 4, 17 is a prime number.
The reasoning is now explicit and verifiable.
Self-Consistency: Taking CoT Further
What if the model's reasoning path leads to an error? Self-Consistency addresses this by generating multiple reasoning chains and selecting the most common answer.
How It Works
- →Prompt the model with the same question multiple times
- →Let it generate different reasoning paths
- →Take the majority vote on the final answer
This ensemble approach reduces the impact of occasional reasoning errors and improves reliability—especially for problems with definitive answers.
When to Use Chain-of-Thought Prompting
CoT is most effective for:
- →Mathematical reasoning — arithmetic, algebra, word problems
- →Logical deduction — puzzles, constraint satisfaction
- →Multi-step analysis — business decisions, comparisons
- →Commonsense reasoning — everyday scenarios requiring inference
When It's Less Useful
- →Simple factual recall ("What's the capital of France?")
- →Creative writing without analytical components
- →Very small models (under ~10B parameters see limited gains)
Key Takeaways
- →Chain-of-Thought prompting makes AI reason step-by-step, improving accuracy on complex tasks
- →Zero-Shot CoT works with just "Let's think step by step"
- →Self-Consistency uses multiple reasoning paths for more reliable answers
- →CoT is most powerful for math, logic, and analytical problems
Ready to Master Advanced Reasoning Techniques?
This article covered the what and why of Chain-of-Thought prompting. But knowing the concept is just the beginning.
In our Module 3 — Chain-of-Thought & Reasoning, you'll learn:
- →How to design production-ready CoT prompt templates
- →Advanced techniques: Tree-of-Thoughts, Chain-of-Verification
- →Hands-on workshops to apply CoT to real business problems
- →How to evaluate and measure reasoning quality
- →When to combine CoT with other techniques for maximum impact
Module 3 — Chain-of-Thought & Reasoning
Master advanced reasoning techniques and Self-Consistency methods.