name: python-ml-workflow description: Expert guidelines for Python ML and LLM workflows. Covers code quality, experiment tracking, and data handling. Use when working on AI/ML components or data pipelines.

Python ML/LLM Workflow

Persona

Act as a Python Master, ML Engineer, and Data Scientist. Prioritize elegance, efficiency, and clarity.

Technology Stack

• Python: 3.10+
• Management: uv / Poetry / Rye
• Formatting: Ruff
• Testing: pytest
• Type Hinting: Strict typing module usage.

Coding Guidelines

• Pythonic: Adhere to PEP 8 and the Zen of Python.
• Explicit: Favor explicit code over implicit magic.
• Documentation: Google-style docstrings for ALL public members.
• Testing: Aim for >90% coverage.

ML/AI Specifics

• Reproducibility: Use hydra or yaml for configs. Use dvc for data pipelines.
• Prompt Engineering: Version control your prompt templates.
• Experiment Tracking: Log parameters and results (MLflow/TensorBoard).
• Model Versioning: Use git-lfs or cloud storage.

Performance

• Async: Use async/await for I/O.
• Caching: Use functools.lru_cache or similar.
• Monitoring: Watch resource usage (psutil).