🤖 LLM-as-Judge Evaluation Framework (Groq)

A fully interactive Streamlit app that uses a Groq-hosted LLM (Llama 3 models) to act as an impartial judge for evaluating and comparing two model outputs.
This tool helps you measure subjective qualities—such as creativity, brand tone adherence, code readability, or any custom metric you define.

Perfect for:

• A/B testing model responses
• Benchmarking prompt variations
• Building custom evaluation frameworks
• Automating subjective model scoring

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🚀 Features

🔍 Compare Model Outputs

Paste two outputs (A and B), describe the task, and let the LLM judge evaluate them.

🎯 Multiple Evaluation Criteria

Choose from built-in presets:

• Creativity
• Brand Tone Adherence
• Code Readability

Or define your own custom criterion (e.g., "Factual Accuracy", "Politeness", "Conciseness", etc.).

📊 Structured Evaluation Output

Every evaluation returns:

• Score for Output A
• Score for Output B
• Judge's explanation for each
• A final winner (A, B, or tie)
• A combined overall comment

🧠 Powered by Groq

Uses the Groq API for ultra-fast inference with Llama 3 models.

🖥️ Clean UI

Built entirely with Streamlit — responsive and easy to use.

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🗂️ Project Structure

llm-as-judge-groq/
├─ app.py # Streamlit web UI
├─ judge.py # Groq evaluation logic
├─ prompts.py # Prompt templates and criteria presets
├─ config.py # Loads environment variables
├─ requirements.txt # Python dependencies
├─ .env.example # Example env file (no real keys)
├─ .gitignore # Ignore secrets and build files
└─ README.md # Project documentation

🧪 Usage Example

1. Describe the task: "Write a friendly tweet announcing our new AI tool."
2. Paste two different outputs from any models.
3. Select a criterion (e.g., Creativity)
4. Click Evaluate Outputs

You’ll receive:

• A score for Output A
• A score for Output B
• Explanations
• A final winner
• Overall comments

🔒 Security Notes

• Never commit your .env file.
• Your .gitignore already ignores .env.
• If a key is ever exposed, regenerate it immediately in the Groq console.

🛠️ Future Improvements (ideas)

• 📝 Multi-criteria scoring (e.g., Creativity + Accuracy + Tone)
• 📥 Upload CSV for batch evaluation
• 📤 Export results (CSV/JSON)
• 🧪 Integrate with experiment tracking (Weights & Biases, LangSmith, etc.)
• 🔌 Plug in more models (OpenAI, Anthropic, HuggingFace, etc.)

⭐ Contributing

• Pull requests are welcome!
• Feel free to submit issues or feature requests.

📄 License

• MIT License — free to use, modify, and distribute.

Enjoy using the LLM-as-Judge Evaluation Framework! If you build something cool on top of this, share it! 🚀

Author

Created by Syed Waleed Ahmed