The 7 Categories of AI Testing Every PM Should Understand
1. Model Performance Testing
What it tests: Whether your AI model performs within acceptable bounds
Key metrics: Accuracy, precision, recall, F1 score, latency
When to run: Before each model deployment, during A/B tests
Example tests:
- Chatbot maintains >90% accuracy on customer service queries
- Recommendation engine click-through rate stays above baseline
- Translation model BLEU score remains above 0.7
2. Functional Testing for AI Outputs
What it tests: Business logic and expected behaviors of AI features
Key metrics: Success rate, error handling, edge case coverage
When to run: Continuously during development, before releases
Example tests:
- AI assistant escalates billing issues 95% of the time
- Content moderation flags inappropriate content within 2 seconds
- Fraud detection system maintains <1% false positive rate
3. Integration Testing
What it tests: How AI features interact with existing systems
Key metrics: Data flow integrity, API compatibility, system performance
When to run: After each integration, before production deployment
Example tests:
- AI recommendations don't suggest out-of-stock items
- Chat responses integrate properly with CRM systems
- ML pipeline processes data without breaking downstream services
4. Load Testing for AI Endpoints