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Contributing to AI Prompt Engineering

Thank you for your interest in contributing! This document provides guidelines and instructions for contributing to this project.

Table of Contents

Code of Conduct

  • Be respectful and inclusive
  • Welcome newcomers and help them learn
  • Focus on constructive feedback
  • Respect different viewpoints and experiences

Getting Started

  1. Fork the repository

  2. Clone your fork:

    git clone https://github.com/your-username/AI-Prompt-Engineering.git
cd AI-Prompt-Engineering

  3. Set up development environment:

    pip install -r requirements.txt

  4. Create a branch:

    git checkout -b feature/your-feature-name

How to Contribute

We welcome contributions in many forms:

Bug Reports

When reporting bugs, please include:

  • Clear description of the issue
  • Steps to reproduce
  • Expected vs actual behavior
  • Environment details (OS, Python version, etc.)
  • Error messages or screenshots

Template:

## Bug Description
Brief description of the bug

## Steps to Reproduce
1. Step one
2. Step two
3. See error

## Expected Behavior
What should happen

## Actual Behavior
What actually happens

## Environment
- OS: [e.g., Windows 10]
- Python: [e.g., 3.9]
- Version: [e.g., 1.0.0]

## Additional Context
Any other relevant information

Feature Requests

When requesting features, please include:

  • Clear description of the feature
  • Use case and motivation
  • Proposed implementation (if applicable)
  • Examples of how it would be used

Template:

## Feature Description
Brief description of the feature

## Use Case
Why is this feature needed?

## Proposed Implementation
How should this work?

## Examples
Provide examples of usage

## Additional Context
Any other relevant information

Documentation Improvements

  • Fix typos or clarify language
  • Add missing documentation
  • Improve examples
  • Translate to other languages

🔧 Code Contributions

  • Fix bugs
  • Implement new features
  • Improve performance
  • Refactor code
  • Add tests

Development Setup

Prerequisites

  • Python 3.8+
  • pip or conda
  • Git

Installation

# Clone repository
git clone https://github.com/ElaMCB/AI-Prompt-Engineering.git
cd AI-Prompt-Engineering

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install development dependencies (if any)
pip install -r requirements-dev.txt

Running Tests

# Run all tests
pytest

# Run specific test file
pytest tests/test_validator.py

# Run with coverage
pytest --cov=notebooks tests/

Running the Dashboard

streamlit run streamlit_app.py

Pull Request Process

  1. Update Documentation:

    • Update README.md if needed
    • Add/update API documentation
    • Include code comments

  2. Test Your Changes:

    • Ensure all tests pass
    • Test manually if needed
    • Check for linting errors

  3. Commit Messages:

    • Use clear, descriptive messages
    • Reference issue numbers if applicable
    • Follow conventional commits format: 
      feat: Add new prompt validator feature
fix: Resolve issue with A/B testing
docs: Update API reference

  4. Create Pull Request:

    • Provide clear description
    • Reference related issues
    • Include screenshots if UI changes
    • Request review from maintainers

  5. Respond to Feedback:

    • Address review comments
    • Make requested changes
    • Update PR as needed

Coding Standards

Python Style

  • Follow PEP 8
  • Use type hints where appropriate
  • Write docstrings for all functions/classes
  • Keep functions focused and small
  • Use meaningful variable names

Code Formatting

# Use black for formatting (if installed)
black notebooks/

# Use isort for import sorting (if installed)
isort notebooks/

Example Code Style

"""
Clear module/class description.
"""

from typing import Dict, List, Optional


class ExampleClass:
    """Class description."""
    
    def __init__(self, param: str):
        """
        Initialize class.
        
        Args:
            param: Description of parameter
        """
        self.param = param
    
    def example_method(self, input_data: str) -> Dict:
        """
        Method description.
        
        Args:
            input_data: Description of input
        
        Returns:
            Dictionary with results
        
        Raises:
            ValueError: If input is invalid
        """
        if not input_data:
            raise ValueError("Input cannot be empty")
        
        return {"result": "example"}

Adding New Features

1. Plan Your Feature

  • Discuss in issues first (if major change)
  • Design the API/interface
  • Consider backward compatibility
  • Plan tests

2. Implement

  • Write code following standards
  • Add docstrings
  • Include type hints
  • Add error handling

3. Test

  • Write unit tests
  • Test edge cases
  • Test error handling
  • Manual testing

4. Document

  • Update API reference
  • Add usage examples
  • Update README if needed
  • Add to changelog

5. Submit

  • Create PR with description
  • Reference related issues
  • Request review

Project Structure

AI-Prompt-Engineering/
├── notebooks/           # Core tools and utilities
├── docs/               # Documentation
├── examples/           # Example code and tutorials
├── tools/              # Additional tools
├── streamlit_app.py    # Streamlit dashboard
├── requirements.txt    # Dependencies
└── README.md          # Main documentation

Where to Add Code

  • Core tools: notebooks/
  • Documentation: docs/
  • Examples: examples/
  • Tests: tests/ (create if needed)
  • Configuration: Root directory

Guidelines

Commit Messages

  • Use present tense ("Add feature" not "Added feature")
  • Be specific and clear
  • Reference issues: "Fix #123"
  • Separate subject from body with blank line

Good:

feat: Add production validator framework

Implements comprehensive production validation with consistency,
robustness, edge case, and performance testing.

Closes #45

Bad:

update code

Pull Requests

  • One logical change per PR
  • Keep PRs small and focused
  • Provide clear description
  • Reference related issues
  • Update documentation

Questions?

  • Open an issue for questions
  • Check existing issues/PRs first
  • Be patient and respectful

Recognition

Contributors will be:

  • Listed in CONTRIBUTORS.md
  • Mentioned in release notes
  • Credited in documentation

Thank you for contributing!