We’ve seen that large language models (LLMs) are capable of generating amazing texts. But when we give them complex problems, they sometimes respond too directly, without showing the kind of reasoning we’d expect from a human being.
That’s where a revolutionary technique comes in: Chain-of-Thought prompting.
What is Chain-of-Thought Prompting?
Imagine you have to solve a tricky riddle. You wouldn’t immediately blurt out the answer, right? First, you’d analyze the problem, break it down into simpler parts, and think through each step until you reached a solution.
Chain-of-Thought (CoT) prompting helps LLMs reason in exactly that way.
What does it consist of?
It’s a technique where you ask the model to explain its reasoning step by step before giving the final answer.
Instead of just asking for the solution, you ask it to show how it got there: what data it uses, what formulas it applies, and how it logically connects the information.
This is especially useful for complex problems that require multiple steps.
How does it work?
Basically, instead of saying:
“Give me the answer to this problem”,
you say:
“Explain how you reach the answer.”
Example
Standard prompt:
What’s the next number in the sequence 2, 4, 8?
Model’s answer: 16
Chain-of-Thought prompt:
Explain step by step how to get the next number after 2, 4, 8.
Answer: The first number is 2. The second is 4, which is 2×2. The third is 8, which is 4×2. The rule seems to be “multiply by 2.” So 8×2 = 16.
With this technique, we don’t just get the answer—we also get the reasoning behind it.
Techniques for Using Chain-of-Thought Prompting
Here are a few ways to apply it:
- “Let’s think step by step”
This simple phrase is often enough to trigger reasoning in the model. - Guiding questions
Include prompts like:- What are the important facts?
- What can we infer?
- How are the pieces of information connected?
- Few-shot prompting with CoT examples
Provide one or two examples in the prompt that show step-by-step reasoning. The model will mimic the pattern. - Problem decomposition
For tough problems, ask the model to break the task down into parts and solve each one before giving the final answer.
Why is Chain-of-Thought Important?
- Higher accuracy on complex problems
Helps the model give better answers when logic, inference, or planning is needed. - Better insight into reasoning
By seeing the steps, we can judge whether the model reasoned correctly. - Easier error correction
If the answer is wrong, we can see where the reasoning went off track. - More human-like thinking
The steps make the process more natural—closer to how a person would reason. - Transparency
We can see how the model arrived at the answer. - Versatility
Works for math, logic, decision-making, data analysis, and more.
Practical Examples
1. Simple math problem
pythonCopiaModifica<code>import openai
# Insert your API key
openai.api_key = "YOUR_API_KEY"
# Prompt with chain-of-thought
prompt = (
"Solve the following math problem step by step:\n"
"Problem: A train travels 300 kilometers in 3 hours. What is its average speed?\n\n"
"Instructions:\n"
"1. Find the formula for average speed.\n"
"2. Plug in the values.\n"
"3. Do the math and explain the result.\n"
"Answer:"
)
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a math expert who explains each step of your reasoning."},
{"role": "user", "content": prompt}
]
)
print(response.choices[0].message.content)
</code>Code language: PHP (php)Explanation:
The prompt guides the model to use the formula for speed (speed = distance / time), substitute the values (300 / 3), and explain how it arrives at the result (100 km/h).
2. Complex example with multiple steps
pythonCopiaModifica<code>import openai
openai.api_key = "YOUR_API_KEY"
messages = [
{"role": "system", "content": (
"You are an expert in analyzing complex problems. Use chain-of-thought to explain each step of your reasoning clearly."
)},
{"role": "user", "content": (
"Example:\n"
"A company has different departments with different quarterly results. We need to understand how the increased marketing budget impacts sales.\n\n"
"Suggested steps:\n"
"1. List departments and sales data.\n"
"2. Connect marketing spending to sales variation.\n"
"3. Assess whether there is a correlation between budget increase and sales.\n\n"
"Now solve this problem using chain-of-thought:\n"
"Problem: A $5,000 ad campaign led to an increase of 150 units sold, each at $20. Explain how to calculate the ROI step by step."
)}
]
response = openai.ChatCompletion.create(
model="gpt-4",
messages=messages
)
print(response.choices[0].message.content)
</code>Code language: JavaScript (javascript)Explanation:
The prompt asks the model to explain how to calculate ROI:
- Calculate total revenue (150×20 = $3,000),
- Compare it to the investment ($5,000),
- Then apply the ROI formula:
(revenue–investment)/investment×100(revenue – investment) / investment × 100(revenue–investment)/investment×100
Best Practices for CoT
- Be specific:
Clearly state what steps you want the model to follow. - Give examples:
Add detailed examples to show the model how to behave. - Provide a good initial context:
The first message should explain what kind of expert the model should act as. - Test and improve:
If the output isn’t what you expected, tweak the prompt and try again.
The Future of Reasoning in AI
Chain-of-Thought prompting is a big step forward in how we interact with language models.
We don’t just get answers—we get the path to those answers.
Whether you’re solving math problems or analyzing complex data, CoT helps you get inside the model’s “mind” and obtain more clear, reliable, and well-reasoned responses.
