Problem
Prompt and model changes were hard to compare consistently over time.
Project Case Study
LLM Evaluation Lab
Evaluation framework that compares prompts, models, and retrieval configurations.
ShippedPythonPytestPandasAWS
Goal
Build a repeatable scoring pipeline for experimentation and regression prevention.
Architecture Overview
System shape and flow
- Test dataset with versioned scenarios
- Scoring adapters for relevance and factuality
- Report output for trend tracking
Key Features
- Regression suites
- Cost metrics
- Experiment snapshots
Tradeoffs and Design Decisions
- Evaluation maintenance overhead
- Requires disciplined dataset curation
Challenges
- Choosing representative test scenarios
- Reducing false positives in quality checks
Results and Lessons Learned
- Improved confidence in model updates
- Reduced guesswork in prompt iterations
Next Steps
- Add human review workflow
- Publish architecture notes