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Copilot Instructions for Mastering LLMs Workshop

This document provides development guidelines and best practices for contributors working on the Mastering LLMs Workshop repository.

Python Version Requirements

  • Use Python 3.11 or above for all code development
  • Ensure compatibility with modern Python features and type hints
  • Leverage new Python 3.11+ features where appropriate (e.g., improved error messages, performance enhancements)

Development Environment

  • Leverage Jupyter Lab wherever possible for:
    • Interactive development and experimentation
    • Workshop tutorials and demonstrations
    • Data exploration and visualization
    • Prototyping LLM implementations

Jupyter Best Practices

  • Use .ipynb files for workshop modules and tutorials
  • Include clear markdown documentation within notebooks
  • Use meaningful cell outputs and visualizations
  • Keep notebooks focused on specific learning objectives

File Organization and Naming

Meaningful Filenames

  • Use descriptive, lowercase filenames with underscores (snake_case)
  • Include version numbers or module identifiers where relevant
  • Examples:
    • 01_introduction_to_llms.ipynb
    • 02_prompt_engineering_basics.ipynb
    • llm_utils.py
    • data_preprocessing.py

Module-wise Workshop Structure

Organize content into logical modules:

|──README.md
|──llm.txt
|──LICENSE
├──docs/
    ├── assets/
    ├── module_01_lm_fundamentals/
    ├── module_02_llm_building_blocks/
    ├── module_03_instruction_tuning_and_alignment/
    ├── module_04_llm_apps/

Documentation Synchronization

README File Maintenance

  • Always cross-check new additions with the README file
  • Keep changes in sync between implementation and documentation
  • Update README.md whenever:
    • New modules or workshops are added
    • Dependencies change
    • Installation instructions are modified
    • Learning objectives are updated

Module-wise README File

  • Always analyze code files (.py, .ipynb) in the module and understand key topics covered
  • The module specific README.md should cover:
    • A title of the module
    • A short description of the module
    • A table of contents with hyperlinks to all notebooks. The hyperlink should be named in a meaningful way.
    • A block quote at the end mention which model was used to generate the README with a timestamp
    • A horizontal line to close it off.

Documentation Standards

  • Use clear, concise language
  • Include code examples where relevant
  • Provide step-by-step instructions
  • Link to relevant external resources
  • Maintain a consistent tone and style

LLM.txt Maintenance

Keep llm.txt Updated

  • Update llm.txt with each significant change to the repository
  • Document:
    • New files and modules added
    • Dependencies and their purposes
    • Workshop structure changes
    • Key learning resources
    • Setup and installation changes

LLM.txt Content Guidelines

  • Provide a comprehensive overview of the repository
  • Include current project structure
  • List key technologies and frameworks used
  • Document overview of topics covered in each module
  • Include links to relevant resources and documentation

Code Quality Standards

General Guidelines

  • Write clean, readable, and well-documented code
  • Use type hints for function parameters and return values
  • Follow PEP 8 style guidelines
  • Include docstrings for all functions and classes
  • Write meaningful variable and function names

Testing

  • Include unit tests for utility functions
  • Test notebook examples and code snippets
  • Validate that all workshop materials work end-to-end

Version Control

  • Make atomic commits with descriptive messages
  • Use conventional commit format where appropriate
  • Keep commits focused on single logical changes
  • Update documentation in the same commit as code changes

Workshop-Specific Guidelines

Learning Objectives

  • Clearly define learning objectives for each module
  • Include practical exercises and hands-on components
  • Provide both theoretical background and practical implementation
  • Include real-world use cases and examples

Resource Management

  • Optimize for computational efficiency in workshops
  • Provide alternatives for different computational environments
  • Include resource requirements and recommendations
  • Consider cloud-based alternatives for resource-intensive tasks

Accessibility

  • Ensure content is accessible to different skill levels
  • Provide prerequisite information clearly
  • Include multiple learning paths where appropriate
  • Support both beginner and advanced participants

Collaboration Guidelines

  • Review and test workshop materials before merging
  • Ensure all external links and resources are accessible
  • Validate that installation instructions work across different environments
  • Coordinate with README updates when making structural changes
  • Maintain consistency in style and approach across all modules