Welcome to the next stage of our journey into Advanced Techniques in Prompt Engineering. We will focus on the Chain-of-Thought method. This approach not only enhances the model's output but also aids in attaining precise answers by guiding the model through a logical sequence of reasoning.

The Chain of Thought method is a compelling technique that encourages the model to "think aloud" as it reaches an answer. This technique is particularly useful when dealing with complex problems that can benefit from step-by-step reasoning. Unlike most humans, who by adulthood know that the best way to think through a complex problem is to think step-by-step, LLMs don't have this instinctive understanding. Explicitly prompting the model to detail its thought process has been proven to lead to more accurate and helpful responses.

Currently, there are reasoning models, like GPT-O1, that use the chain of thought in their generation process without any specific prompting. These models are better at answering questions and drawing conclusions due to their inherent reasoning capabilities. However, models like GPT-4 do not automatically employ chain of thought unless explicitly instructed. While reasoning models excel in logical reasoning tasks, non-reasoning models might exceed them in tasks like writing code, where structured and precise outputs are required.

Designing prompts that leverage the Chain of Thought method requires clarity in the query and precision in the expectations. The goal is to guide the model away from merely guessing or jumping to conclusions and towards methodically working through the problem. This strategy not only aids in achieving more reliable outputs but also enables users (and developers) to understand the rationale behind the model's answers.

One task that LLMs are notoriously bad at is mathematical computations. Let's see an example:

While after running this many times you might get the right answer, more often than not, the LLM will make a mistake and respond with numbers like 191,087, 191,987, etc., none of which are correct.

Now let's see what happens when we change it to apply Chain-of-Thought. The most standard way to apply this method is to add a requirement to think step-by-step. Other acceptable options include:

• begin by carefully analyzing the problem
• show your work before responding
• etc.

Sample output:

And that response is spot on.

When it comes to Chain-of-Thought, the more you can help guide the thought process of the LLM in the right direction, the more likely you are to get a high-quality and accurate response. For example, the way we can improve mathematical calculations is to hint that using relevant round numbers could help with computations, like this:

Sample Output:

Having learned how to structure your prompts to efficiently utilize the Chain of Thought method, it's now your turn to experiment with this approach. Remember, the goal is to encourage the model to think through problems, step by step, and conclude with concise and accurate answers. Let's begin the practice!