AI model testing
What is AI model testing?
AI model testing involves running a variety of checks to assess model quality before and after deployment. These checks can evaluate:
- Accuracy or output consistency
- Response to edge cases
- Sensitivity to data drift
- Fairness across subgroups
- Alignment with business goals or user expectations
Why it matters in AI/ML
Without testing, AI models may:
- Fail silently in production
- Deliver biased or unfair outcomes
- Perform well on validation data but poorly in the real world
Robust testing:
- Catches regressions before they impact users
- Ensures continuous learning doesn't degrade performance
- Helps teams build trust in AI outcomes
Types of AI model tests
1. Behavioral tests
- Evaluate model response to specific prompts or inputs
- Test edge cases, ambiguous data, or adversarial scenarios
2. Fairness and bias tests
- Measure outcomes across sensitive attributes (e.g., gender, age, location)
- Detect disparities and flag risk areas
3. Drift and robustness testing
- Simulate changes in input data over time
- Assess whether predictions remain stable
4. LLM-specific testing
- Use LLM-as-a-Judge to score output quality
- Test reasoning, tone, safety, and structure across prompt variants
5. Regression testing
- Compare model versions to track improvement or degradation
Related
AI model testing is the foundation of responsible deployment. Every update should be tested, measured, and verified—before users experience it.