PromptPilot: Autonomous Prompt Debugger & Optimizer PromptPilot is a powerful tool for AI prompt engineers to debug, optimize, and test prompts using the free OpenRouter API. Built with a professional Streamlit UI, it analyzes prompt issues, suggests improvements, and provides side-by-side testing with OpenRouter's free-tier models (e.g., LLaMA 3.1 8B Instruct). Features
Prompt Analysis: Identifies issues like vagueness, lack of context, or missing specificity using chain-of-thought reasoning. Prompt Optimization: Generates improved prompts with role-based engineering and few-shot learning. Side-by-Side Testing: Compares original and optimized prompt outputs across multiple iterations using OpenRouter. Modern UI: Streamlit-based interface with Tailwind CSS for a user-friendly experience. Extensible: Modular design with support for adding new OpenRouter models and prompt examples.
Installation
Clone the repository: git clone https://github.com/yourusername/promptpilot.git cd promptpilot
Install dependencies: pip install streamlit requests python-dotenv
Set up your OpenRouter API key in a .env file: OPENROUTER_API_KEY=your_openrouter_api_key
Run the app: streamlit run app.py
Usage
Open the app in your browser (default: http://localhost:8501). Paste your prompt in the input box. Select an OpenRouter model and configure testing options (e.g., iterations, debug mode). Click "Analyze & Optimize" to view issues, optimized prompt, and side-by-side test results. Download results as a JSON file for further analysis.
File Structure
app.py: Main Streamlit application with the UI and core logic. prompt_utils.py: Prompt analysis, optimization, and testing functions. models_config.py: OpenRouter model configurations and client initialization. examples/: JSON files with predefined good and bad prompt examples. README.md: Project documentation.
Notes
Ensure your OpenRouter API key is valid and has access to free-tier models. The free tier has rate limits, so testing iterations are capped at 3 to avoid exceeding quotas. Replace YOUR_OPENROUTER_API_KEY in models_config.py or use a .env file for security.
Contributing Contributions are welcome! Please open an issue or submit a pull request on GitHub. Ensure your code follows PEP 8 and includes tests where applicable. License MIT License. See LICENSE for details.