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- Add Frank v6 core personality and base commands - Install 7 reasoning skills (CRAFT, CoT, ToT, RAG, Markdown, Mermaid, Advanced Reasoning) - Install 5 specialties (DevOps, ITIL, Data Analysis, Prompt Engineering, SCCM) - Update copilot-instructions.md with v6 integration guide - Add comprehensive architecture documentation - Migrate style.mermaid.instructions.md from instructions/ to skills/ - Remove deprecated .github/instructions/ files (migrated to skills/) - Remove obsolete create-commit.msg.prompt.md
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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.