PUSH_TO_GITHUB.md

🚀 Push to GitHub - Complete Instructions

Step 1: Create a GitHub Repository

1. Go to https://github.com/new
2. Sign in to your GitHub account
3. Fill in the form:
   • Repository name: togmal-prompt-analyzer
   • Description: "Real-time LLM capability boundary detection using vector similarity search"
   • Public: Selected
   • Initialize this repository with a README: Unchecked
4. Click "Create repository"

Step 2: Push Your Local Repository

After creating the repository, you'll see instructions. Use these commands in your terminal:

cd /Users/hetalksinmaths/togmal
git remote add origin https://github.com/YOUR_USERNAME/togmal-prompt-analyzer.git
git branch -M main
git push -u origin main

Replace YOUR_USERNAME with your actual GitHub username.

What You'll Have on GitHub

Once pushed, your repository will contain:

Core Implementation

• benchmark_vector_db.py - Vector database for difficulty assessment
• demo_app.py - Gradio web interface
• fetch_mmlu_top_models.py - Script to fetch real benchmark data

Documentation

• COMPLETE_DEMO_ANALYSIS.md - Comprehensive analysis of the system
• DEMO_README.md - Demo instructions and results
• GITHUB_INSTRUCTIONS.md - These instructions
• README.md - Main project documentation

Test Files

• test_vector_db.py - Test script with real data examples
• test_examples.py - Additional test cases

Configuration

• requirements.txt - Python dependencies
• .gitignore - Files excluded from version control

Key Features Demonstrated

Real Data vs Mock Data

• Before: All prompts showed ~45% success rate (mock data)
• After: System correctly differentiates difficulty levels:
  • Hard prompts: 23.9% success rate (HIGH risk)
  • Easy prompts: 100% success rate (MINIMAL risk)

11 Test Questions Analysis

The system correctly categorizes:

• Hard Questions (20-50% success):
  • "Calculate the quantum correction to the partition function..."
  • "Prove that there are infinitely many prime numbers"
  • "Statement 1 | Every field is also a ring..."
• Easy Questions (80-100% success):
  • "What is 2 + 2?"
  • "What is the capital of France?"
  • "Who wrote Romeo and Juliet?"

Recommendation Engine

Based on success rates:

• <30%: Multi-step reasoning with verification
• 30-70%: Use chain-of-thought prompting
• >70%: Standard LLM response adequate

Live Demo

Your demo is running at:

• Local: http://127.0.0.1:7861
• Public: https://db11ee71660c8a3319.gradio.live

Next Steps After Pushing

1. Add badges to README (build status, license, etc.)
2. Create GitHub Pages for project documentation
3. Set up CI/CD for automated testing
4. Add more benchmark datasets
5. Create releases for different versions

Need Help?

If you encounter any issues:

1. Check that you're using the correct repository URL
2. Ensure you have internet connectivity
3. Verify your GitHub credentials are set up
4. Make sure you've replaced YOUR_USERNAME with your actual GitHub username

For additional support, refer to:

• GitHub Documentation