# Building a Production LLM Quiz Application with Vertex AI ## Project Overview This case study details the evolution of a trivia quiz application from a traditional static database-driven system to an LLM-powered dynamic quiz generator. The project began as a weekend project in 2016 to showcase Progressive Web App features but transformed significantly with the introduction of large language models in 2023. ## Technical Architecture ### Core Components - UI Server: Built with Flutter for cross-platform support - API Server: Python Flask application running on Cloud Run - Database: Firestore for real-time updates and document storage - AI Platform: Google Vertex AI platform utilizing Gemini models - Image Generation: Imagen model for quiz-related imagery ### Key Features - Dynamic quiz generation from any topic - Multi-language support - Real-time multiplayer functionality - Automated image generation - Quiz validation and quality assessment ## LLM Integration Journey ### Initial Implementation - Started with PaLM model in March 2023 - Transitioned to Gemini Pro and later Gemini Ultra - Implemented prompt engineering for structured quiz generation - Added support for multiple languages - Integrated image generation capabilities ### Prompt Engineering Learnings - Specific prompting required for consistent JSON output - Balance needed between prompt complexity and result quality - Language support achieved through simple prompt modifications - Image generation prompts kept simple with negative prompting for better results ## Production Challenges and Solutions ### Data Quality and Validation - Implemented LLM-based validation system - Achieved 94% accuracy in quiz validation with Gemini Ultra - Developed automated testing framework for quiz quality - Created benchmark system using open trivia database ### Error Handling and Resilience - Defensive coding practices for handling LLM failures - Implementation of fallback mechanisms - Batch processing for efficient API usage - Caching strategies for common responses ### Architecture Best Practices - Model version pinning for stability - Parallel processing of quiz and image generation - Real-time updates using Firestore - Containerized deployment with Cloud Run ## Testing and Validation Framework ### Validation Strategy - Use of LLMs to evaluate LLM outputs - Automated validation pipeline - Background processing for quiz validation - Statistical confidence scoring for quiz accuracy ### Quality Metrics - Improvement from 70% accuracy (Gemini Pro) to 91% (Gemini Ultra) - Validator accuracy of 94% with Gemini Ultra - Implementation of automated test suites - Continuous validation of generated content ## Technical Considerations ### Security and Input Handling - Implementation of profanity filters - Input sanitization before LLM processing - Safe prompt construction - Error handling for malformed outputs ### Performance Optimization - Batch processing of validation requests - Parallel processing where possible - Caching strategies - Asynchronous validation processing ## Key Learnings ### LLM Integration - Need for defensive coding practices - Importance of prompt versioning - Balance between automation and human oversight - Significance of testing and validation ### Best Practices - Version control for prompts - Automated testing frameworks - Quality metrics and monitoring - Error handling and resilience ### Future Improvements - Enhanced grounding techniques - User-controlled temperature settings - Expanded language support - Advanced validation methods ## Infrastructure and Deployment ### Google Cloud Integration - Use of Cloud Run for containerized deployment - Firestore for real-time database functionality - Vertex AI for LLM integration - Container-based architecture for scalability ### Monitoring and Maintenance - Continuous validation of quiz quality - Automated testing pipelines - Performance monitoring - Error tracking and logging ## Conclusion The project demonstrates the transformation possible with LLM integration while highlighting the importance of robust engineering practices. The team successfully overcame challenges in prompt engineering, validation, and testing to create a production-ready application that leverages the latest in AI technology while maintaining high standards of quality and reliability.