Mastering Complex Problem-Solving with AI: The Power of Chain Thought Prompting

Imagine having to tackle a big problem. It feels overwhelming, doesn't it?

That's why you could break it down into smaller steps. This is easier said than done a lot of the time. This activity alone may paralyze you.

You shouldn't give up, though. Look for ways to break a problem down until you can manage it.

And yes, you can use AI for it. Still, how are you going to communicate with AI to get the results you seek?

Answer: You can teach it. Use the chain of thought prompting to teach AI. You can give AI the way to the answer rather than the answer itself.

You can give AI an example with an answer, though. AI will learn the logic and apply the process to your actual problem.

So let's break this down:

Understanding the chain of thought prompting

The mechanics of the chain of thought prompting

Applications in AI and NLP

Understanding the Chain of Thought Prompting

In simple terms, the chain of thought prompting is a method to guide AI systems. I can feed AI a complex problem. I can also walk it through the steps to solving the problem.

Here's my best example of this. I use ChatGPT to help with Excel formulas. I love and admire the power of Excel. I'm not the best at the formulas, though. So I ask ChatGPT to come up with the formula.

Because I don't know how to structure the formula, I have to walk ChatGPT through the steps of what I want. For instance, if I want to add information on a cell found on cells in other spreadsheets. My chain of thought prompting may look like this:

• A1, Sheet 1 = [name]; A1, sheet 2 = [name]
• Look for values matching A1, sheet 1 on A1, sheet 2
• Copy matching value onto C1, sheet 1
• Example: A1, sheet 1 = Jose Montanez; A1, sheet 2 = Jose Montanez; copy A1, Sheet 2 on to C1, sheet 1; C1, sheet 1 = Jose Montanez

Because I don't know every formula function, I have to break it down for AI to understand. This also helps me communicate what I want.

You can do the same with any complex problem.

The Mechanics of The Chain of Thought Prompting

Let's break down this process.

1. Start with a complex problem. Do you have a challenging problem or question that requires more than just a simple answer? This is the problem you want to use for this.

2. Guide AI through the steps. If you try to ask AI for an answer, it will likely be inaccurate. Instead, break down the problem into smaller steps. You can even ask AI for help in breaking down the problem if you are stuck.

3. Arrive at the solution. Either AI reaches the solution or you can feed it a logical process to the solution. AI will use it to solve your actual problem. For instance, I can give the answer for AI to recognize the process. AI will apply the same reasoning to find the solution to your problem.

Use my example above to appreciate how you must break down a task. Of course, this doesn't guarantee that you will get the right answer. You have to continue to feed it information and correct its output.

Applications in AI and NLP

AI uses natural language processing (NLP) to better perform tasks. NLP enables AI systems to better understand and interpret the context of the problem. This is critical for solving problems requiring highly logical reasoning.

NLP helps AI break down and analyze the components of these complex problems. You want to include NLP techniques when dealing with the following tasks:

• Multi-step reasoning
• Analogy-making
• Logical deductions

This leads to more accurate outputs.

NLP also helps AI communicate in a way humans can understand. This is another reason why people don't want AI-written messages, blog posts, or books. They are not the same unless a human is part of the process.

The chain of thought with NLP is more than a tool or technique. It's a way to enhance the collaboration between AI and humans. This leads to greater trust between AI's outputs and users.

Conclusion

Chain of thought prompting offers a powerful way to tackle complex problems. It allows you to break down the problem into steps which allows you to understand it better. It also allows AI to learn the process from you for a more accurate output.

The challenge of inputting the correct information is always present, though. Always double-check your work and AI's output. A small error can create a compounding effect in the long run.