# Chain-of-Thought Prompting

import {Screenshot} from 'components/screenshot'
import COT from '../../img/cot.png'
import ZEROCOT from '../../img/zero-cot.png'


## Chain-of-Thought (COT) Prompting

<Screenshot src={COT} alt="COT" />

Image Source: [Wei et al. (2022)](https://arxiv.org/abs/2201.11903)

Introduced in [Wei et al. (2022)](https://arxiv.org/abs/2201.11903), chain-of-thought (CoT) prompting enables complex reasoning capabilities through intermediate reasoning steps. You can combine it with few-shot prompting to get better results on more complex tasks that require reasoning before responding.

*Prompt:*
```
The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.
A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False.

The odd numbers in this group add up to an even number: 17,  10, 19, 4, 8, 12, 24.
A: Adding all the odd numbers (17, 19) gives 36. The answer is True.

The odd numbers in this group add up to an even number: 16,  11, 14, 4, 8, 13, 24.
A: Adding all the odd numbers (11, 13) gives 24. The answer is True.

The odd numbers in this group add up to an even number: 17,  9, 10, 12, 13, 4, 2.
A: Adding all the odd numbers (17, 9, 13) gives 39. The answer is False.

The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. 
A:
```

*Output:*
```
Adding all the odd numbers (15, 5, 13, 7, 1) gives 41. The answer is False.
```

Wow! We can see a perfect result when we provided the reasoning step. In fact, we can solve this task by providing even fewer examples, i.e., just one example seems enough:

*Prompt:*
```
The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.
A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False.

The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. 
A:
```

*Output:*
```
Adding all the odd numbers (15, 5, 13, 7, 1) gives 41. The answer is False.
```

Keep in mind that the authors claim that this is an emergent ability that arises with sufficiently large language models.

## Zero-shot COT Prompting

<Screenshot src={ZEROCOT} alt="Zero-shot COT" />

Image Source: [Kojima et al. (2022)](https://arxiv.org/abs/2205.11916)

One recent idea that came out more recently is the idea of [zero-shot CoT](https://arxiv.org/abs/2205.11916) (Kojima et al. 2022) that essentially involves adding "Let's think step by step" to the original prompt. Let's try a simple problem and see how the model performs:

*Prompt:*
```
I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with?
```

*Output:*
```
11 apples
```

The answer is incorrect! Now Let's try with the special prompt. 

*Prompt:*
```
I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with?

Let's think step by step.
```

*Output:*
```
First, you started with 10 apples.
You gave away 2 apples to the neighbor and 2 to the repairman, so you had 6 apples left.
Then you bought 5 more apples, so now you had 11 apples.
Finally, you ate 1 apple, so you would remain with 10 apples.
```

It's impressive that this simple prompt is effective at this task. This is particularly useful where you don't have too many examples to use in the prompt.