Building a Thinking Model from a Non Thinking Model Using Chain of Thought

Artificial Intelligence models like GPT or other large language models are often described as powerful tools for generating text, answering questions, and solving problems. However, these models do not truly think the way humans do. They rely on patterns in data and statistical predictions. To make them appear more like “thinking” systems, researchers and developers use a technique called Chain of Thought.

What is a Non Thinking Model
A non thinking model processes input and produces output without showing the steps it took to reach that output. For example, if you ask a model, “What is 27 multiplied by 14” it might respond with the correct answer but it will not tell you how it got there. The reasoning process is hidden inside the model’s statistical weights and it cannot explain itself clearly unless explicitly designed to do so.

What is Chain of Thought
Chain of Thought is a prompting and training method where the model is encouraged to show its reasoning step by step. Instead of jumping directly to the answer, the model explains the process it is following. For the multiplication example above, the model might say:

• First, multiply 27 by 10 to get 270

• Then, multiply 27 by 4 to get 108

• Add 270 and 108 to get 378

By breaking down the problem, the model simulates a reasoning process similar to how a human would solve it.

Why Chain of Thought Helps

1. Improved Accuracy – Complex tasks often require intermediate steps. When a model generates those steps, it is less likely to skip over important details.

2. Transparency – Showing reasoning makes it easier for humans to understand why a model reached a certain conclusion.

3. Debugging – If a model makes a mistake, the step-by-step explanation makes it easier to identify where it went wrong.

4. Learning and Generalization – When a model practices step-by-step reasoning, it becomes better at solving problems that require multiple stages of thought.

Building a Thinking Model from a Non Thinking Model
To convert a non thinking model into a more reasoning focused one using Chain of Thought, the following approach can be used:

1. Prompt Engineering
   Craft prompts that explicitly ask the model to show its reasoning. Instead of asking “What is the answer” ask “Explain your reasoning step by step and then provide the answer.”

2. Few Shot Examples
   Provide the model with examples of problems and their detailed solutions in the prompt. This teaches it the style of answering with reasoning steps.

3. Fine Tuning
   Train the model on datasets that include both the question and a reasoning sequence leading to the answer. This makes the model naturally produce chain-of-thought style responses.

4. Self Consistency Sampling
   Generate multiple reasoning paths for the same question and compare their results. Select the most common answer among them. This reduces the effect of occasional mistakes in reasoning.

5. Reasoning Scaffolds
   Break complex problems into smaller subproblems and feed them to the model in stages. The output of one stage becomes the input for the next, guiding the model through a structured thought process.

Example Transformation

Without Chain of Thought:
Q: If I have 15 apples and eat 4, how many are left
A: 11

With Chain of Thought:
Q: If I have 15 apples and eat 4, how many are left
A: I start with 15 apples. I eat 4, so I subtract 4 from 15. 15 minus 4 equals 11. The answer is 11.

This small difference turns the model’s response from a direct output into an understandable reasoning sequence.

Challenges in Using Chain of Thought

• Overhead in Response Length – Step-by-step answers take more space and time to generate

• Risk of Overthinking – For very simple problems, long reasoning can be unnecessary and slow

• Data Quality – If the training examples are incorrect, the model will learn faulty reasoning patterns

Future Possibilities
Chain of Thought can be combined with external tools like calculators, logic checkers, or knowledge bases to improve accuracy even further. In the future, AI models may use Chain of Thought internally for all complex reasoning while keeping simple answers short when no detailed explanation is needed.

Conclusion
A non thinking model can be turned into a reasoning capable model by encouraging it to produce intermediate thought steps using Chain of Thought. This approach improves accuracy, transparency, and trust. While the model is still not truly thinking like a human, the simulation of reasoning makes it more useful for a wide range of tasks from problem solving to explaining concepts in detail.