In today’s video, we are going to start our series that I have called ‘The Perfect Prompt Principles.’ So basically, this series will cover techniques that you can learn to improve your prompt game and how you can solve different problems using different techniques. Today, we are going to start with the ‘Chain of Thought’ principle.
The ‘Chain of Thought’ principle is a technique that can be used to solve certain types of problems. It involves breaking down a problem into smaller sub-problems and solving them step by step. This approach is similar to how humans solve problems, as we don’t just jump to the final answer but go through each step in a chain.
To demonstrate this principle, let’s take an example using ChatGPT 3.5. Imagine the following problem: Michael, a 31-year-old man from America, is visiting a famous museum in France. While looking at the museum’s most famous painting, he is reminded of his favorite cartoon character from his childhood. The problem is to determine the country of origin of the object that this cartoon character usually holds in his hands.
This problem cannot be solved by simply looking at the last sentence and making a guess. We need to think through the problem step by step. So, the first step is to identify Michael’s location, the type of museum he is visiting, and the most famous painting in the museum. Then, we need to determine the artist of the painting and Michael’s favorite cartoon character. Finally, we can determine the country of origin of the object.
By using the ‘Chain of Thought’ principle, we can prompt ChatGPT 3.5 with each sub-problem and solve them one by one. For example, we can prompt the model to identify Michael’s location, and it will likely answer that he is visiting the Louvre Museum in France. Then, we can prompt it to identify the most famous painting, and it will likely answer that it is the Mona Lisa by Leonardo da Vinci. We can continue this process to determine the artist, the cartoon character, and the object’s country of origin.
This approach allows us to break down the problem into manageable steps and solve them systematically. It helps us improve the output of the language model and solve problems that couldn’t be solved before.
In addition to ChatGPT 3.5, we can also apply the ‘Chain of Thought’ principle to other language models like GPT-4 or Advanced Data Analysis. The key is to think through the problem step by step and prompt the model with each sub-problem.
In conclusion, the ‘Chain of Thought’ principle is a powerful technique for problem-solving with language models. By breaking down a problem into smaller steps and solving them systematically, we can improve the accuracy and output of the model. This principle can be applied to various types of problems and can help us find solutions that couldn’t be found through a single prompt. Stay tuned for more episodes in ‘The Perfect Prompt Principles’ series, where we will explore other techniques for prompt engineering.
Thank you for watching, and don’t forget to check out my other videos for more insights on language models and problem-solving techniques!
