Metrics question

Lovable wants to ship quickly and validate early. If you were launching a first version of vulnerability detection or AI output review, what MVP would you ship first, what tradeoffs would you make on coverage versus false positives, and which post-launch metrics would tell you whether to expand, revise, or roll it back?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Whether you can scope an MVP for a safety-adjacent AI feature with a defensible coverage-versus-false-positive tradeoff and clear rollback signals.

Scope the MVP narrowly: detect a small set of well-understood, high-severity vulnerability classes (for example exposed secrets or obvious injection risks) rather than attempting broad coverage on day one.
Set the tradeoff explicitly: favor lower false-positive rate over broad coverage at launch, since noisy false flags will train users to ignore the feature entirely.
Design the review UX to be low-friction, presenting flags with clear explanation and an easy dismiss or fix action, so even the false positives that do occur do not create heavy user cost.
Define post-launch metrics: flag precision (percent of flags that are real issues), user action rate on flags (fixed versus dismissed), and, most importantly, actual vulnerability incidents in published apps before and after launch.
Set expansion criteria: broaden coverage to more vulnerability classes only after precision holds steady on the initial set.
Set rollback criteria: if dismiss rate stays very high or user complaints about noise spike, narrow scope further or pause expansion rather than pushing forward.

States the coverage-versus-false-positive tradeoff plainly and picks a side with reasoning, low false positives first, since trust in the feature depends on it.
Names a concrete, narrow starting scope, exposed secrets or obvious injection risks, instead of a vague 'detect vulnerabilities'.
Proposes both a precision metric and a real-world outcome metric, incidents before and after, connecting the feature to actual security value.
Sets explicit rollback criteria, not just expansion criteria, showing the launch is genuinely reversible if it does not work.

Trying to cover broad vulnerability classes in the MVP instead of a narrow, high-confidence starting set.
No metric connecting the feature to real-world security outcomes, only measuring flags raised.
No rollback criteria if the feature turns out to be noisy or low-value.

How would you decide which vulnerability classes to add next after the initial launch?
What would you do if precision is high but users still ignore the flags?
How would you validate the feature is not giving users false confidence in unflagged code?