# Chain-of-Thought (CoT) Prompting Engine Guide

## 1. Prompting Techniques

There are three primary methods for implementing CoT prompting, each with its own advantages.

### 2.1. Few-Shot CoT

This is the standard approach where you provide the model with a few examples (demonstrations) that include a question, a step-by-step reasoning process (the chain of thought), and the final answer.

**When to Use:** Use this method for complex tasks where the reasoning structure is consistent and providing diverse, high-quality examples can significantly guide the model. This is the most powerful method but requires manual effort to create the demonstrations.

**Example Prompt Structure:**

Q: [Question 1]

A: [Step-by-step reasoning for Question 1]. The answer is [Answer 1].

Q: [Question 2]

A: [Step-by-step reasoning for Question 2]. The answer is [Answer 2].

Q: [New Question]

A:

The file demos/multiarith_manual provides a practical example of the JSON structure for these hand-crafted demonstrations.

### 2.2. Zero-Shot CoT

A surprisingly effective and simple method that requires no examples. By appending the phrase **"Let's think step by step"** to the end of a question, the model is triggered to generate a reasoning chain before giving the final answer.

**When to Use:** This is an excellent starting point for any reasoning task. It's highly effective for its simplicity and is particularly useful when you don't have time to create few-shot examples.

**Example Prompt Structure:**

Q: [New Question]

A: Let's think step by step.

The api.py script in the repository shows how this is implemented by setting a cot_trigger argument. The Jupyter notebooks (try_cot.ipynb and try_cot_colab.ipynb) demonstrate its application and output.

### 2.3. Automatic CoT (Auto-CoT)

Auto-CoT is an advanced technique designed to automate the creation of diverse and effective demonstrations for Few-Shot CoT, eliminating the manual effort. As detailed in the project's README.md, it works in two main stages.

**Stage 1: Question Clustering**

* The system takes a dataset of questions and groups them into several clusters based on semantic similarity.

**Stage 2: Demonstration Sampling**

* It selects a representative question from each cluster.
* It then uses **Zero-Shot CoT** to automatically generate a reasoning chain for each selected question.

This process, detailed in run_demo.py, ensures that the examples are both diverse (by sampling from different clusters) and accurate, creating a robust set of demonstrations for the model to learn from. The output of this process can be seen in the demos/multiarith_auto file.

**When to Use:** Use Auto-CoT when you need the high performance of Few-Shot CoT on a large dataset of questions but want to avoid the time-consuming and potentially suboptimal process of manually writing demonstrations.

## 3. Implementation in the Repository

The provided repository contains a full implementation of these techniques.

* **api.py**: A core file that defines the cot function, which can be called with different methods: "zero_shot", "zero_shot_cot", "manual_cot", and "auto_cot".
* **run_inference.py**: The main script for running experiments. It loads a dataset, constructs prompts based on the chosen method, and generates answers.
* **run_demo.py**: This script implements the Auto-CoT process by clustering questions and generating demonstrations.
* **try_cot.ipynb**: A Jupyter Notebook that provides a quick and clear way to test and compare the outputs of each CoT method.

To get started, refer to the README.md and the try_cot_colab.ipynb for a guided walkthrough.

## 4. References

* [Amazon Science Repo on CoT](https://github.com/amazon-science/auto-cot)
* [CoT Example](http://../../knowledge/ai.generated/examples/example.CoT-Prompting.md)