1. Problem Statement

Design a comprehensive platform for LLM application development that enables teams to evaluate, test, and monitor AI systems throughout the development lifecycle. The platform should support prompt engineering, dataset management, automated evaluation, experiment tracking, and production observability.

Core Problem

LLM development lacks systematic tooling for evaluation and quality assurance. Teams struggle with:

• Inconsistent evaluation: Manual testing leads to unreliable results
• Prompt drift: No version control for prompts and configurations
• Production blindness: Limited visibility into AI system performance
• Collaboration barriers: Difficulty sharing experiments and results across teams

2. Functional Requirements

Core Features

1. Prompt Management
   • Version control for prompt templates
   • Template variables and composition
   • A/B testing capabilities
   • Rollback and deployment tracking
2. Dataset Management
   • Upload and organize evaluation datasets
   • Synthetic data generation
   • Dataset versioning and lineage
   • Golden dataset curation
3. Evaluation Engine
   • Automated evaluation execution
   • Custom scoring functions
   • Parallel evaluation processing
   • Reproducible experiment runs
4. Experiment Tracking
   • Compare multiple models/prompts
   • Statistical significance testing
   • Experiment history and reproducibility
   • Collaborative result sharing
5. Production Observability
   • Real-time AI system monitoring
   • Performance degradation alerts
   • Cost and usage tracking
   • Error analysis and debugging
6. CI/CD Integration
   • Git webhook triggers
   • Automated evaluation pipelines
   • Quality gates for deployments
   • Regression testing

Nice-to-Have Features

• Custom evaluator marketplace
• Fine-tuning pipeline integration
• Multi-modal evaluation support