AutoPrompt MVP Benchmark

A Python-based benchmark system for evaluating AutoPrompt performance using Google's Gemini AI

Features • Quick Start • Results • Documentation

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🎯 Overview

AutoPrompt MVP Benchmark is an advanced prompt engineering system that automatically optimizes prompts for improved AI extraction performance. It compares a baseline approach with an intelligent AutoPrompt engine that dynamically generates and scores multiple prompt variants.

Key Highlights

• 🚀 15-20% Accuracy Improvement over baseline approaches
• 🧪 Automated Testing with 60%+ code coverage
• 📊 Visual Analytics with comprehensive performance charts
• ⚡ Production-Ready with retry logic, rate limiting, and error handling
• 🔄 CI/CD Pipeline with automated testing on every commit

🎬 Interactive Demo

Live comparison showing AutoPrompt achieving 20.5% higher confidence than baseline approach

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✨ Features

Feature	Description
Baseline Pipeline	Standard single-prompt review processing
AutoPrompt Engine	Dynamic prompt variant generation with automatic scoring
Intelligent Scoring	Heuristic-based quality assessment with optional LLM scoring
Comprehensive Evaluation	Metrics for accuracy, edge cases, and confidence levels
Rate Limit Handling	Built-in support for API free tier constraints
Visualization Suite	Automated chart generation for result analysis

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🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• Google Gemini API key (Get one here)
• (Optional) Docker for containerized deployment

Installation

1. Clone the repository

   git clone https://github.com/aayush-1o/auto-Prompt.git
   cd auto-Prompt

2. Install dependencies

   pip install -r requirements.txt

3. Configure API key

   Create a .env file in the project root:

   GEMINI_API_KEY=your_api_key_here

Running the Benchmark

# Run the full benchmark
python main.py

# Generate visualizations after benchmark completes
python visualize_results.py

🎨 Interactive Demo (Streamlit)

Launch the interactive web demo:

streamlit run app.py

Then open your browser to http://localhost:8501 to try the system interactively!

🐳 Docker Deployment

Option 1: Docker Compose (Recommended)

# Run Streamlit app
docker-compose up

# Run benchmark (use benchmark profile)
docker-compose --profile benchmark up autoprompt-benchmark

Option 2: Docker (Manual)

# Build image
docker build -t autoprompt .

# Run Streamlit app
docker run -p 8501:8501 -v $(pwd)/.env:/app/.env autoprompt

# Run benchmark
docker run -v $(pwd)/results:/app/results -v $(pwd)/.env:/app/.env autoprompt python main.py

📊 Jupyter Analysis

Open the analysis notebook:

jupyter notebook notebooks/analysis.ipynb

Note: The benchmark processes 20 reviews with built-in rate limiting for free tier API usage.

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📊 Results

Performance Comparison: AutoPrompt vs Baseline

The system demonstrates significant improvements across all metrics:

• Overall Accuracy: +15-20% improvement
• Edge Case Handling: +25% better performance on ambiguous reviews
• Failure Rate: -50% fewer malformed outputs
• Confidence Score: Higher average confidence in predictions

Sample Output

🎯 AUTOPROMPT EVALUATION REPORT
================================================================
BASELINE RESULTS:
  overall_accuracy: 72.50
  product_accuracy: 75.00
  sentiment_accuracy: 80.00
  edge_case_accuracy: 45.00

AUTOPROMPT RESULTS:
  overall_accuracy: 88.75
  product_accuracy: 92.50
  sentiment_accuracy: 95.00
  edge_case_accuracy: 70.00

IMPROVEMENT:
  overall_accuracy: +16.25%
  edge_case_accuracy: +25.00%
================================================================

Visualizations are automatically generated in results/ directory after running the benchmark.

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📁 Project Structure

autoprompt/
├── .github/
│   └── workflows/
│       └── ci.yml              # CI/CD pipeline
├── config/
│   └── prompt_config.yaml      # Prompt templates and candidates
├── data/
│   ├── reviews.csv             # Sample review data
│   └── ground_truth.json       # Labeled ground truth
├── results/                    # Benchmark outputs (generated)
│   ├── baseline_results.json
│   ├── autoprompt_results.json
│   ├── benchmark_report.json
│   └── *.png                   # Visualization charts
├── src/
│   ├── autoprompt.py           # AutoPrompt engine with variant generation
│   ├── baseline.py             # Baseline single-prompt pipeline
│   ├── evaluator.py            # Performance evaluation metrics
│   ├── config_loader.py        # Secure configuration loading
│   └── utils.py                # Data models and utilities
├── tests/
│   ├── test_utils.py           # Unit tests for utilities
│   └── test_evaluator.py       # Unit tests for evaluator
├── main.py                     # Entry point
├── visualize_results.py        # Chart generation script
└── requirements.txt            # Python dependencies

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🧪 Testing

Run the test suite:

# Run all tests
pytest

# Run with coverage report
pytest --cov=src --cov-report=html

# Run specific test file
pytest tests/test_utils.py -v

Tests are automatically run via GitHub Actions on every push.

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🔧 Configuration

The system uses YAML-based configuration in config/prompt_config.yaml:

• Instruction candidates: Different ways to request extraction
• Target info candidates: Variations in specifying output fields
• Model settings: Temperature, model versions, scoring options

Modify these to experiment with different prompt strategies.

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📈 How It Works

AutoPrompt Pipeline

1. Variant Generation: Creates multiple prompt variations from candidate pools
2. Parallel Evaluation: Tests each prompt variant on the review
3. Quality Scoring: Scores each result using heuristics (+ optional LLM)
4. Best Selection: Returns highest-scoring extraction
5. Early Stopping: Terminates when acceptable quality threshold is reached

Baseline Pipeline

• Single, static prompt for all reviews
• Direct extraction without optimization
• Serves as performance comparison baseline

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

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📧 Contact

Ayush - @aayush-1o

Project Link: https://github.com/aayush-1o/auto-Prompt

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⭐ Star this repo if you find it helpful!

Made with ❤️ using Google Gemini AI