LANDMARK · 7 MIN · BUILD
Testing AI systems
After this landmark, you can build a test suite for an AI feature that covers correctness, adversarial robustness, and graceful failure, not just the happy path.
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This concept is shared, but the Everyday lens is less central here.
You can still explore it. We’re showing the shared explanation and a related practical view without hiding the knowledge.
Testing an AI system extends traditional software testing (does it work) with categories that don’t apply to deterministic code: does it fail predictably when it doesn’t know something, rather than fabricating an answer; does it hold up against adversarial input (a jailbreak attempt, an injected instruction) rather than silently complying; and does its behavior stay stable across the many equivalent phrasings of the same request, since small prompt or input changes can shift non-deterministic outputs in ways traditional tests don’t anticipate. A test suite that only checks the happy path with typical inputs will miss most of what actually goes wrong in production.
This is the canonical concept. It stays the same across learner lenses so personalization never changes the underlying facts.
◌ EVERYDAY LENS
What this looks like for you
Not your layer directly, but it’s why some AI products handle an odd or adversarial request gracefully (a clear ‘I don’t know’ or a safe refusal) and others confidently produce nonsense or comply with something they shouldn’t.
MAKE A DECISION
A team has a test suite for their AI assistant that checks it gives correct answers on 50 common questions, all passing. What’s missing before this is production-ready?
CARRY THISFor one AI feature you build or maintain, list its test coverage across three categories: correctness, uncertainty handling, and adversarial robustness. Which is thinnest?