Chain of Thoughts prompting method
Many people ask me the question of which prompting methods are easy and most effective, and one of them is CoT. What is this method and how does it work?
Imagine you have one big problem and then you divide it into several smaller problems and solve each one gradually. That's what CoT does to a Large Language Model. The main point of Chain-of-Thoughts is guiding a large language model through a series of reasoning steps to reach a solution for something, like how we solve problems in our heads. We will always solve a problem gradually, step by step, and that's how CoT works.
Here's a simple example. Let's say we have a math problem: 'Roger has 5 tennis balls and buys 2 more cans containing 3 balls each. How many does he have now?' instead of jumping straight to 'the answer is 11', the model would think aloud and write: 'Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 balls. 5 + 6 = 11'. This way of creating answers helps to remove hallucinations and makes the answer more correct.
Why is this method good?
- Improves complex reasoning: this method helps AI a lot with complex reasoning tasks such as arithmetic, commonsense, and symbolic reasoning. It can even help with debugging code!
- Offers a peek into AI's thought process: it's like seeing what the model is thinking in its head, which is very useful for debugging and understanding its reasoning path. For example, the model might make a mistake in the middle of reasoning and this can help us find and correct the error.
- Versatile & powerful: this method works on various types of tasks where traditional methods like the role method struggle.
How to create a prompt using this method? First and foremost, it's important to write examples. Examples are the most important in prompts and without examples, nothing succeeds. Write a few examples of how an LLM can reason and that's it. Instructions for CoT can be written in just a few sentences.
However, besides CoT, there are other methods that are even more efficient.