Chain-of-thought reasoning (Simply Explained)

What is Chain-of-thought reasoning (CoT)?

“Chain of thought (CoT) mirrors human reasoning, facilitating systematic problem-solving through a coherent series of logical deductions”.
IBM: https://www.ibm.com/think/topics/chain-of-thoughts

Essentially, CoT encourages an AI model to take intermediate steps before getting to the final answer. Instead of going straight to the solution, the model will explain its thought process in a sequence, making sure each step follows the previous one.

Here’s a simple example to explain the process of CoT:

Question :
A company’s product costs $25 to make and sells for $40. They sold 1,000 units last month. They also spent $5,000 on marketing. Did the company make a profit?

Chain of Thought Reasoning:

Revenue per unit = $40
Total revenue = 1,000 units × $40 = $40,000
Cost per unit = $25
Total production cost = 1,000 units × $25 = $25,000
Additional marketing cost = $5,000
Total expenses = $25,000 + $5,000 = $30,000
Profit = Revenue − Expenses = $40,000 − $30,000 = $10,000

Réponse :
Yes, the company made a $10,000 profit.

How Does Chain-of-Thought Reasoning Work?

Prompting: Users guide the AI to think step-by-step by adding instructions like “Show your reasoning” or “Explain each step” to their prompts.
Logical Steps: The AI then works through the problem one step at a time, laying out its thought process in a way that’s easy to follow.
Final Answer: After walking through the steps, the AI gives its final answer, which is usually more accurate because it followed a clear and structured path.

Why Is Chain-of-Thought Reasoning Important?

Better Accuracy: Taking time to break down the problem helps the AI avoid mistakes, especially in tasks that need multiple steps like math, logic puzzles, or complex decisions.
Clearer Explanations: It becomes easier to see how the AI reached its answer, which builds trust and helps with troubleshooting if something goes wrong.
More Human-Like Thinking: Step-by-step reasoning makes AI feel more natural and relatable, as it mirrors how people typically solve problems.

Benefits of CoT Reasoning

Handles complex, multi-step problems more effectively.
Reduces errors and hallucinations in AI-generated responses.
Makes AI systems more interpretable and trustworthy.

Limitations

Slower Responses: More steps mean longer answers and increased computational cost.
Prompt Quality: Effectiveness depends on how well the prompts are designed.
Scalability: Can struggle with very specialized or technical domains without tailored instructions.

Résumé

Chain-of-though is an interesting way for models to solve prompt in a human like manner. It can be very useful to handle complex tasks and reduce errors. However, it has its limitations when it comes to speed and accuracy when the instructions are of poor quality.

Learn more about Chain-of-thought reasoning:

IBM : https://www.ibm.com/think/topics/chain-of-thoughts
Invisible: https://www.invisible.co/blog/how-to-teach-chain-of-thought-reasoning-to-your-llm
Orq: https://orq.ai/blog/what-is-chain-of-thought-prompting
BotPress: https://botpress.com/blog/chain-of-thought

Raisonnement par chaîne de pensée (Explication simple)