"Chain-of-Thought": The Key Behind Our Thought Process

By: Miguel Oviedo
Date: April 12, 2023

Artificial Intelligence (AI) continues to advance at an incredible pace, and one of the most innovative techniques gaining traction is "Chain of Thought." This approach not only enhances the cognitive capabilities of AI models but also facilitates the interpretation of their internal workings, representing a major step toward more transparent and reliable models.

In this article, we will explore what "Chain-of-Thought" is, how it works, and why it is important. Additionally, we will use a clear and detailed example to illustrate its functionality in an easy-to-understand way.

What is "Chain-of-Thought"?

In AI, Chain-of-Thought refers to breaking down complex problems into smaller, more manageable steps. This structured approach enables AI models to address and solve challenges that were previously too intricate. It’s akin to how a human would mentally break down a problem into smaller steps before solving it, one stage at a time.

Chain-of-Thought

Similar to the succession of mental connections that form as we progress through our thought process, "Chain-of-Thought" can be linear, where thoughts connect one after another, or branching, where thoughts diverge in different directions.

How Does "Chain-of-Thought" Work?

"Chain-of-Thought" begins with an initial thought, which could be a question, problem, or idea. From this initial thought, our mind tends to connect it to other related or derived thoughts. This sequence of mental connections can happen quickly and automatically or may require deeper reflection. As the thought process progresses, new thoughts connect to previous ones, forming a chain of interconnected ideas.

Chain-of-Thought

Practical Example

Consider a simple math problem that requires multiple steps of reasoning: determining whether the sum of odd numbers in a group results in an even number. Using the Chain-of-Thought technique, an AI model might approach this problem as follows:

Question: Does the sum of odd numbers in this group result in an even number? Group of numbers: 15, 32, 5, 13, 82, 7, 1.

Step-by-step reasoning:

Identify the odd numbers in the group: 15, 5, 13, 7, 1.
Add these numbers: 15 + 5 + 13 + 7 + 1 = 41.
Determine if the result (41) is even or odd.

Result:

The sum of the odd numbers is 41, which is an odd number. Therefore, the statement is false.

This example demonstrates how Chain-of-Thought allows AI to break down a problem into comprehensible steps, facilitating the verification of each stage and explaining the final result.

Why Chain-of-Thought Is Key in Using ChatGPT Effectively

Chain-of-Thought is crucial for maintaining clear and coherent communication with ChatGPT. When interacting with ChatGPT, it’s important to maintain a logical structure in questions or comments so the model can understand and respond appropriately. If the Chain-of-Thought breaks or becomes confusing, the generated responses may be incoherent or irrelevant.

A clear example of the importance of Chain-of-Thought in using ChatGPT is when asking a series of questions or providing information in multiple stages.

For instance, suppose you’re interacting with ChatGPT to get information about a recipe. If you ask an initial question about ingredients, followed by a question about preparation steps, and then a question about cooking time, it’s essential to maintain a clear and sequential Chain-of-Thought so the model can understand and respond accurately to each question.

Chain-of-Thought

The Chain-of-Thought is setting a new standard for AI development, promoting models that are not only powerful but also understandable and trustworthy. As this technique is refined and more widely adopted, we are likely to see AI even more integrated into everyday solutions, enhancing everything from complex automated systems to intelligent personal assistants.