Chain of Thought (CoT) Prompting

The Key to Smarter AI Reasoning

Source: Image generated using Microsoft Designer

Introduction

Chain of Thought Prompting (CoT) is a technique used in natural language processing (NLP) to improve the reasoning abilities of AI systems. While AI is good at processing information quickly, it often struggles with complex problem-solving tasks that require reasoning through intermediate steps. CoT helps bridge this gap by guiding AI to think through problems more logically — just like we do.

Introduced in Wei et al. (2022), chain-of-thought (CoT) prompting enables complex reasoning capabilities through intermediate reasoning steps. You can combine it with few-shot prompting to get better results on more complex tasks that require reasoning before responding.- [1]

In this article, let’s break down how Chain of Thought prompting works, why it is valuable, and how it is being applied in real-world AI systems.

What is Chain of Thought Prompting?

At its core, Chain of Thought Prompting involves asking AI to walk through its thought process step by step. Instead of providing a final answer directly, we prompt AI to generate intermediate steps that lead to a solution.

Source: Image from Wei et al. (2022)

This approach mimics how humans solve problems: we don’t just jump to conclusions; instead, we think through the logic, gather facts, and then make decisions.
For instance, if you’re solving a math problem like

What is 12 times 8?

you might break it down to steps like:

1. What is 10 times 8?
2. What is 2 times 8?
3. Now, add the two results together.

By breaking it down, AI is less likely to make mistakes and is more capable of handling complex questions.

The Value of Structured Reasoning in AI

Most AI systems are trained to generate responses based on patterns and data they’ve been exposed to. However, without a structured thought process, these systems can struggle with tasks that require reasoning across multiple steps. Chain of Thought Prompting helps AI to:

Reduce errors: By guiding AI to think through its answers step by step, we reduce the chances of skipping crucial steps that could lead to errors.

Handle complexity: It makes it easier for AI to tackle multi-faceted problems, such as math, logic puzzles, and even some creative tasks.

Mimic human reasoning: CoT helps AI mirror the way humans reason, making its responses feel more intuitive and aligned with human expectations.

Breaking Down the Process of Chain of Thought

When we use Chain of Thought prompting, the process generally follows these steps:

1. Model Input (The Prompt): When you provide an input (like a question or task) to the AI, instead of asking for the final answer right away, you include instructions that encourage the model to “think through” the problem. The AI is prompted to reason through the task step by step, breaking the problem down into smaller, manageable parts.
2. Tokenization and Embeddings: The AI model processes your prompt by first converting the input into tokens (pieces of text or words) and mapping them to vectors in a high-dimensional space. These vectors represent semantic meaning, and the AI uses them to understand relationships between words and concepts.
3. Intermediate Reasoning: With a CoT prompt, the model is trained to generate a sequence of intermediate reasoning steps. This involves predicting and chaining these intermediate tokens; essentially, it’s “thinking out loud.” For instance, in math, instead of directly outputting “120,” it might first output intermediate steps like “multiply 60 by 2” before arriving at the final result.
4. Autoregressive Output: Many large language models (like GPT) are autoregressive, meaning they generate text one token at a time, each token conditioned on the preceding ones. In CoT, the model generates intermediate reasoning steps token by token until it has built the full reasoning path.
5. Final Output: After generating all intermediate steps, the model arrives at a final conclusion. This final output is based on the accumulation of those intermediate steps, ensuring that the reasoning is grounded in a logical flow.

For example, consider a customer asking, “How do I return my order?” A basic AI might provide a generic return policy. However, with Chain of Thought, the AI would first examine the customer’s order details, such as the product type and return window. It would then guide the customer through the relevant steps based on their specific situation — like verifying whether the item is still eligible for return or if a return shipping label is required — resulting in a more personalized and accurate response.

Implementation using python:

import openai

# Set your OpenAI API key
openai.api_key = 'your-api-key-here'

# Function to prompt GPT with Chain of Thought
def chain_of_thought_prompt(prompt):
    response = openai.Completion.create(
        engine="text-davinci-003",  # Using GPT-3 (davinci) model
        prompt=prompt,
        max_tokens=200,  # Control the length of the response
        temperature=0.5,  # Controls randomness; adjust for more creative answers
        stop=["\n"]  # Stop generating when a new line is encountered
    )
    return response.choices[0].text.strip()

# Simulate Customer Service Query: How to return an order
customer_query = "How do I return my order?"

# A simple function to simulate customer details (e.g., return window, product type, etc.)
def get_customer_details():
    # This could be dynamically fetched from the customer's order system.
    return {
        "order_number": "123456",
        "product": "wireless headphones",
        "purchase_date": "2023-01-15",
        "return_window_days": 30
    }

# Extract customer details
customer_details = get_customer_details()

# Create a Chain of Thought prompt for customer service
cot_prompt = f"""
A customer has asked: "{customer_query}"

Customer details:
Order Number: {customer_details['order_number']}
Product: {customer_details['product']}
Purchase Date: {customer_details['purchase_date']}
Return Window: {customer_details['return_window_days']} days

Now, think through the process step by step and guide the customer through the return process.
1. First, check if the order is within the return window.
2. If it is within the return window, explain the steps for initiating the return.
3. If it's outside the return window, explain the policy and suggest alternatives (like contacting support).
"""

# Get the AI's response with step-by-step reasoning
response = chain_of_thought_prompt(cot_prompt)

# Print the chatbot's response
print(f"Chatbot Response:\n{response}")

Chatbot Response:

Let's think through this step by step:
1. First, let's check if the order is within the return window. 
   The order was placed on 2023-01-15, and the return window is 30 days. 
   Today's date is 2023-02-01, which is within the return window.
2. Since the order is eligible for return, the next step is to guide the 
   customer on how to return the item:
   - Step 1: Go to our website and log into your account.
   - Step 2: Navigate to the "My Orders" section.
   - Step 3: Find order #123456 and click "Request Return."
   - Step 4: Choose the reason for the return and print the return shipping label.
   - Step 5: Pack the wireless headphones and drop off the return package at the nearest shipping center.
3. If the return window had passed, I would explain that the return policy 
   no longer applies. In that case, the customer could contact our support 
   team to request a special exception or look into the warranty options.

Different Types of Chain of Thought Prompting

While the basic idea behind CoT is always about step-by-step reasoning, there are a few different ways to structure and guide this reasoning depending on the task or model.

Image Source: Kojima et al. (2022)

Standard Chain of Thought (CoT): This is the most straightforward form of CoT, where the AI is asked to think through the problem step by step. This is typically used for tasks like solving math problems, logic puzzles, or factual questions.

Example:
Input: What is 72 divided by 8?
CoT Prompt: First, consider how many times 8 fits into 72. 
            8 goes into 72 nine times.
Output: 9

Few-Shot Chain of Thought (Few-Shot CoT): In a few-shot setting, the model is given a few examples of step-by-step reasoning before being asked to solve a new problem. This helps the model understand how to break down the problem logically by seeing reasoning in action.

Example:
Input: What is the sum of 17 and 8?

Few-shot CoT Prompt:
Example 1: 
What is 15 + 7? First, break down 15 into 10 + 5, and 7 into 3 + 4.
Add them together: (10 + 3) = 13, and (5 + 4) = 9. 
The final sum is 22.

Example 2: 
What is 30 + 15? Break it down: 30 + 10 = 40, then 40 + 5 = 45.

New Input: What is 17 + 8?
CoT Output: First, split 17 into 10 and 7, and 8 into 5 and 3. 
            Add 10 + 5 = 15, then 7 + 3 = 10. 
The total sum is 25.

Zero-Shot Chain of Thought (Zero-Shot CoT): This method doesn’t require any example steps. The AI is prompted to reason through a new problem without prior examples. While more challenging, this approach relies on the model’s training to infer how to break down the problem on its own.

Example:
Input: What is 48 multiplied by 6?
Zero-Shot CoT Prompt: Think about how to multiply 48 by 6 step by step. 
                      First, break 48 into 40 and 8. Multiply 40 by 6 to 
                      get 240, and 8 by 6 to get 48. Now, add the two results 
                      together: 240 + 48 = 288.
Output: 288

Recursive Chain of Thought (Recursive CoT): This involves breaking down a complex problem into smaller sub-problems and solving them recursively. It is particularly useful for tasks like language translation or tasks requiring multiple stages of decision-making.

Example:
Input: Translate ‘I have two apples’ into French.
Recursive CoT Prompt: Break down the sentence: 'I' is 'je' in French.
                      'have' is translated as 'ai'.
                      'two' is 'deux' in French.
                      'apples' is 'pommes' in French.
Output: J'ai deux pommes.

Self-Consistency Chain of Thought (Self-Consistent CoT): This approach involves generating multiple chains of thought and selecting the answer that appears most frequently. It’s useful when the problem is ambiguous or has multiple potential solutions.

Example:
Input: What is the largest prime number less than 100?
Self-Consistency CoT Prompt: Let’s try two paths: 
1. I’ll list prime numbers below 100: 97, 89, 83, etc. 
   So, the largest prime is 97. 
2. Check if 101 is prime — it’s greater than 100, 
   so I’ll confirm that 97 is the largest.
Output: 97

Challenges with Chain of Thought Prompting

Despite its usefulness, there are challenges in implementing CoT effectively:

Complexity: For particularly intricate problems, defining the right intermediate steps can be tricky. AI might still struggle to determine what’s most relevant to solve the problem.

Computational Resources: More steps often mean more computation. The AI has to process more information, which can lead to longer response times or increased resource consumption.

Training Data: To make CoT work well, the AI needs to be trained on large datasets where these intermediate steps are explicitly shown. If the AI hasn’t encountered enough examples of step-by-step reasoning, it might not always perform well.

Conclusion

Chain of Thought Prompting is a straightforward but effective way to enhance AI’s reasoning. By breaking down problems into smaller steps, it reduces errors, boosts problem-solving, and makes AI responses more intuitive. Despite some challenges, this approach is already making an impact in areas like customer service, education, and complex tasks like math and logic. As AI evolves, techniques like CoT will become even more integral, helping AI systems provide deeper insights and more accurate, reliable answers.

If you’re interested in learning more about building AI chatbots, here’s an article that might be interesting to you:

The Art of Building Customer-Facing AI Chatbots

References:

1. Prompting Guide. (n.d.). Chain of thought prompting. Prompting Guide. Retrieved February 1, 2025, from https://www.promptingguide.ai/techniques/cot
2. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2023, January 10). Chain-of-thought prompting elicits reasoning in large language models. arXiv.org. https://doi.org/10.48550/arXiv.2201.11903
3. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2023, January 29). Large language models are zero-shot Reasoners. arXiv.org. https://doi.org/10.48550/arXiv.2205.11916