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This project is an attempt at comparing the performance of Large Language Models when prompted with regular vs chain-of-thought prompting techniques.

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Chain-of-Thought-Prompting

Project Setup Guide

This guide will walk you through setting up and running this project locally from scratch.

Prerequisites

  • Python 3.11
  • Git (for cloning the repository)

Initial Setup

1. Install Python 3.11

If you haven't installed Python 3.11 yet:

  • Download Python 3.11 from python.org
  • During installation, make sure to check "Add Python to PATH"
  • Verify installation by running:
python --version  # Should show Python 3.11.x

2. Install pip (Python Package Installer)

Pip usually comes with Python, but if you need to install it:

On Windows:

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py

On macOS/Linux:

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python3 get-pip.py

Verify pip installation:

pip --version

3. Install uv

uv is a fast Python package installer. Install it using pip:

pip install uv

4. Set Up Virtual Environment

Create and activate a virtual environment using uv with Python 3.11:

On Windows:

uv venv -p 3.11
.\.venv\Scripts\activate

On macOS/Linux:

uv venv -p 3.11
source .venv/bin/activate

You should see (.venv) in your terminal prompt indicating the virtual environment is active.

5. Install Project Dependencies

With the virtual environment activated, install the project requirements using uv:

uv pip install -r requirements.txt

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This project is an attempt at comparing the performance of Large Language Models when prompted with regular vs chain-of-thought prompting techniques.

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