Intro:Artificial Intelligence challenges almost every assumption the testing discipline is built on. Traditional testing depends on fixed inputs and predictable logic, but AI systems are adaptive, probabilistic, and context-dependent. That means our classical test cases are no longer stable reference points.
In this 20-minute talk, Nicole van Gijn explores what testing looks like when your system learns, reasons, and occasionally hallucinates. She introduces the AI Quality Grid, a structured framework co-developed with John Kronenberg, that helps define quality attributes, risks, and validation strategies for AI applications. The session bridges theory and practice through concrete examples from a real AI test project, showing how LLM-evals and risk-based thinking can be combined to test prompt robustness, output consistency, and bias control within modern CI/CD pipelines.
Attendees will walk away with a lightweight but actionable structure for AI quality assessment and a new mindset: understanding quality not as a checklist, but as an intelligent, adaptive discipline. Why this topic is relevant:
- AI systems are rapidly entering production pipelines, yet testing methods lag behind.
- Testers and QA leads urgently need practical models to evaluate non-deterministic outputs.
- The AI Quality Grid offers a bridge between AI model evaluation (LLM-evals) and classical test strategy, providing testers with new tools and thinking patterns to stay relevant in the AI era.
