# Python LLM & ML Workflow .cursorrules Prompt File
## Synopsis

This prompt file is designed for senior Python AI/ML engineers specializing in Large Language Model (LLM) applications and Machine Learning (ML) workflow optimization. It provides a comprehensive set of guidelines and best practices for developing high-quality, maintainable, and efficient Python code.

## Tech Stack

- Python 3.10+
- Poetry / Rye
- Ruff
- `typing` module
- `pytest`
- Google Style Docstrings
- `conda` / `venv`
- `docker`, `docker-compose`
- `async` and `await`
- `fastapi`
- `gradio`, `streamlit`
- `langchain`, `transformers`
- (Optional) `faiss`, `chroma`, `mlflow`, `tensorboard`, `optuna`, `hyperopt`, `pandas`, `numpy`, `dask`, `pyspark`
- `git`
- `gunicorn`, `uvicorn`, `nginx`, `caddy`
- `systemd`, `supervisor`

## Key Features

- Emphasizes modular design, code quality, and ML/AI-specific guidelines.
- Focuses on performance optimization, including asynchronous programming and caching.
- Provides detailed coding standards and best practices for Python and FastAPI.
- Includes guidelines for effective documentation, testing, and error handling.
- Tailored for use with the Cursor IDE, but applicable to general Python development.

## Usage

Place this `.cursorrules` file in the root of your project to guide the AI assistant in adhering to these standards and practices.

## Contribution

This prompt file is a collaborative effort, and contributions are welcome. Feel free to suggest improvements or additions to enhance its utility for Python AI/ML development.