The AI's Inner Monologue: Building a Thinking Model with Chain-of-Thought

Introduction

Large Language Models (LLMs) have taken the world by storm, powering everything from chatbots to content creation tools. For many simple tasks, they feel like magic. However, when faced with a complex, multi-step problem, they can sometimes fall short. They might act like a student who knows the final answer but can't show their work-leaving us with a correct, or worse, incorrect, response without any insight into their reasoning.

This is where Chain-of-Thought (CoT) prompting comes in. It’s a powerful technique that transforms an LLM from a simple guesser into a "thinking" model by making it reason step-by-step, just like a human would.¹

Section 1: The "Non-Thinking" Model

So, what exactly is a "non-thinking" model in this context? It's an LLM that, given a complex prompt, attempts to provide a single, direct answer without any intermediate steps. For simple questions, this is great. But for a task that requires logic, calculation, or multiple steps, this "black box" approach can be a major problem.

Without a visible chain of reasoning, it’s impossible to know if the model got the answer right by pure chance or if it's confidently wrong. This makes the model unreliable for tasks that require high accuracy, such as coding, data analysis, or complex problem-solving.

Section 2: The Core of Chain-of-Thought

Chain-of-Thought (CoT) prompting is a simple yet revolutionary idea. Instead of asking the model for a final answer, you prompt it to show its work. By instructing the model to think through the problem step-by-step, you are effectively forcing it to create an internal monologue that mirrors human reasoning.²

Think of it like a math problem. When a teacher asks you to "show your work," they're not just looking for the final number; they want to see the process. The same is true for an LLM. By explaining its own steps, the model is more likely to catch its own mistakes and arrive at a correct, verifiable answer.³ This "inner monologue" becomes a powerful tool for self-correction and improved accuracy.⁴

Section 3: Building the Thinking Model (Practical Example)

Let's see this in action with a simple logical problem.

Example 1: Without CoT

We'll ask the model a multi-step question and see what it does.

Prompt:
Mark has 5 apples. He buys 3 more. He gives 2 apples to his friend. How many apples does Mark have now?

A common response from a non-thinking model might be:

6

While the answer is correct, we have no idea how the model arrived at it. On a more complex problem, this single number would be entirely unreliable.

Example 2: With CoT

Now, let's use the exact same prompt but add a simple instruction.

Prompt:
Mark has 5 apples. He buys 3 more. He gives 2 apples to his friend. How many apples does Mark have now? Let's think step by step.

The response from a thinking model will be entirely different:

Response:
Mark starts with 5 apples.
He buys 3 more, so we add 3 to his total: 5 + 3 = 8.
He gives 2 apples away, so we subtract 2 from his total: 8 - 2 = 6.
Mark has 6 apples now.

By adding a single, simple phrase, we've transformed the model's output from a single number into a clear, verifiable chain of reasoning.

Section 4: Advanced CoT Techniques

Once you've mastered the basic principle, you can explore more advanced CoT methods.

• Zero-Shot CoT: This is what we just did—simply adding a phrase like "Let's think step by step" without providing any examples. This works remarkably well for many models.

• Few-Shot CoT: For more complex or domain-specific tasks, you can provide the model with a few examples of problems and their step-by-step solutions.⁵ This "trains" the model on the exact reasoning process you want it to follow.⁶

Conclusion

Chain-of-Thought is more than just a trick; it's a fundamental shift in how we interact with LLMs. By encouraging an AI to show its work, we move it from a non-thinking guesser to a more reliable, reasoning, and collaborative partner.⁷ Mastering this technique is a key skill for any developer or professional looking to get the most out of their generative AI tools.