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

• ML testing
• LLM test
• Model validation
• Model drift vs. data drift
• GenAI testing

AI model testing is the foundation of responsible deployment. Every update should be tested, measured, and verified—before users experience it.