When we humans face a tough problem, like solving a math puzzle or deciding which laptop to buy, we usually don’t jump straight to the answer. Instead, we walk through our thinking:
“Okay, first I need to figure out the budget, then compare features, then look at reviews...”
That step-by-step thinking helps us make better decisions.

Now imagine teaching AI to do the same.

That’s the idea behind Chain of Thought (CoT) prompting, a technique that helps large language models (LLMs) like ChatGPT become more accurate by simply encouraging them to “think out loud.”

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What is Chain of Thought Prompting?

Chain of Thought prompting is a method where instead of asking a model to give you a final answer right away, you ask it to explain the reasoning process step by step before reaching a conclusion.

Chain of Thought (CoT) prompting is a technique that helps Large Language Models (LLMs) perform complex reasoning tasks by breaking down the problem into a series of intermediate steps*.*

This helps the AI break down problems, catch errors, and make more informed decisions, especially for tasks like:

• Math word problems

• Logic puzzles

• Moral reasoning

• Cause-effect questions

• Multi-step decisions

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Without Chain of Thought vs. With Chain of Thought

Question:
Mary has 5 tennis balls. She buys 2 more cans of tennis balls. Each can contains 3 tennis balls.
How many tennis balls does she have now?

Answer:

Without Chain of Thought

AI:
11 tennis balls.

The answer is correct, but we don’t see how the model arrived at it. It feels more like a guess than a reasoned response.

With Chain of Thought

AI:
Mary starts with 5 tennis balls.
She buys 2 cans, and each can contains 3 tennis balls.
So, 2 × 3 = 6 new balls.
Now, 5 + 6 = 11 tennis balls in total.

Still correct but this time, we can follow the reasoning step-by-step. This becomes especially important for complex problems where transparency and trust matter.

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Working of Chain of Thought Prompting

Start with the question: You present the LLM with the actual question or task you want it to solve.

Break it down: Then, you provide a few-shot sequence of reasoning steps that demonstrate how to approach the problem. These steps are like mini explanations that show the LLM the thought process leading to the answer.

Follow the chain: The LLM uses this “chain of thought” as a guide to reason out its own answer. It analyzes the information, applies the intermediate steps and ultimately generates its own final response.

What’s Happening Under the Hood?

1. The LLM sees the problem and the phrase "Let's think step by step."

2. That triggers its internal reasoning mode, instead of jumping to an answer, it tries to mimic human logical steps.

3. The model generates a chain of intermediate steps based on training examples it's seen.

4. It combines math and logic to walk through the answer path.

5. This structured thought path leads to higher accuracy and better explainability.

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Less Abstraction

Unlike Step-Back Prompting, which zooms out to general concepts and higher-level abstractions, Chain of Thought stays grounded.
It doesn’t try to generalize, it walks directly through the logic path.

This makes it:

• More focused: Best for concrete, well-defined tasks

• Easier to trace: You can follow and debug each step

• Reliable: Helps the model avoid shortcuts or hallucinations

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Code

from google import genai
from openai import OpenAI
from dotenv import load_dotenv
import os
load_dotenv()

system_prompt = """
You are a helpful assistant that thinks step by step.

Q: John has 4 apples. He gives 2 away. How many does he have left?
Let's think step by step.
A: John starts with 4 apples. 
   He gives away 2. 
   So 4 - 2 = 2. 
   He has 2 apples left.
"""


client = OpenAI(
    api_key=os.getenv("GOOGLE_API_KEY"),
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
)

response = client.chat.completions.create(
    model="gemini-2.0-flash",
    n=1,
    messages=[
        {"role": "system", "content": system_prompt},
        {
            "role": "user",
            "content": "If a train travels 60 miles in 1 hour and 30 miles in the next 0.5 hours, what is its average speed?
Let's think step by step."
        }
    ]
)


print(response.choices[0].message.content)

Expected Output

First, find the total distance: 60 + 30 = 90 miles.
Next, find the total time: 1 + 0.5 = 1.5 hours.
Now, divide distance by time: 90 ÷ 1.5 = 60.
So, the average speed is 60 miles/hour.

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Why This Works So Well

Humans often learn and explain through stories and steps. we like to see the “why” behind an answer.
AI, when prompted with Chain of Thought, mimics that same process.

Here’s what makes it powerful:

• Less Guesswork: The AI doesn’t rely on pattern-matching alone; it builds the answer logically.

• Better Accuracy: It makes fewer silly mistakes because each step reinforces the next.

• Explainability: You can actually see how it got to the final answer, which helps you trust it more.

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Wrapping Up

Chain of Thought prompting is like giving the model a thinking voice, not just a way to answer, but a way to reason.
Rather than jumping straight to conclusions, the AI takes a pause, breaks things down, and walks us through its logic just like we would when solving something thoughtfully.

By encouraging step-by-step reasoning, we’re not only improving the model’s accuracy but we’re making its thinking transparent, explainable, and human-like. And in a world where understanding how an answer was reached matters as much as the answer itself, that’s a powerful shift.

Because at the end of the day, whether it's a person or a machine, the smartest answers come from slowing down and thinking things through, one step at a time.

Chain of Thought is more than just a prompt style. It's a mindset. One that leads to better outcomes, deeper trust, and more meaningful interaction.

If this made you rethink how RAG works, you’ll love this follow-up:
👉 RAG Explained: Supercharge Your LLM with Real-Time Knowledge

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