Testing & Quality Assurance
Ensure your AI product delivers consistent, reliable results before launch.
AI-Specific Testing
- Model accuracy testing: Test on held-out datasets, measure precision/recall/F1 scores
- Edge case testing: Unusual inputs, ambiguous queries, adversarial examples
- Output quality evaluation: Manual review of samples, user ratings, domain expert validation
- Consistency testing: Same input should produce similar outputs across multiple runs
- Performance testing: Measure latency, throughput, resource usage under load
- Bias detection: Test across demographics, languages, marginalized groups
- Safety testing: Attempt to generate harmful, offensive, or inappropriate content
Traditional Testing
- • Unit tests for business logic
- • Integration tests for API endpoints
- • End-to-end tests for critical user flows
- • Security testing (penetration testing, vulnerability scanning)
- • Load testing to validate scalability
- • Cross-browser and device testing
User Testing
Alpha testing (internal): Team tests all features, logs issues, validates UX
Beta testing (external): 20-50 real users, gather feedback, measure engagement
Usability testing: Watch users interact with product, identify friction points
A/B testing: Test variations of prompts, UI, features to optimize performance
Key Takeaways
- Test AI outputs manually and automatically—don't rely solely on metrics
- Focus on edge cases and failure modes unique to AI
- Run beta testing with real users before public launch
- Test for bias, safety, and inappropriate outputs proactively