AI & Technical question

How should OpenAI handle hallucinations in ChatGPT for high-stakes use cases like medical or legal questions?

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

AI product judgment on managing model risk in domains where a wrong answer causes real harm.

Segment by stakes: distinguish casual medical or legal curiosity questions from high-stakes ones like dosage or specific legal advice.
Propose detection: a classifier that flags high-stakes queries in medical and legal domains before the model responds.
Propose mitigation for flagged queries: stronger grounding via retrieval from vetted sources, explicit uncertainty language, and a prompt to consult a professional.
Address measurement: track hallucination rate on these domains specifically using expert-graded evals, not general benchmarks.
Confirm with the interviewer whether the focus is product design, model behavior, or both.

Treats high-stakes hallucination as a domain-specific problem, proposing targeted classifiers and retrieval grounding rather than a blanket disclaimer on every answer.
Proposes concrete UX like inline citations to sources for medical or legal claims so users can verify rather than blindly trust.
Names a candidate metric, like expert-reviewed accuracy rate on a sampled set of high-stakes medical queries, marked as an assumption for a target like 98 percent.
Acknowledges the tradeoff: over-flagging drives users to less careful competitors, so friction must be proportional to actual risk.

Proposing a blanket disclaimer on all responses instead of targeted detection and grounding for actually risky queries.
Ignoring that over-restricting responses pushes users toward competitors with no guardrails at all.

How would you build the classifier that detects a high-stakes medical query?
How would you measure whether your mitigation actually reduced real-world harm, not just hallucination rate?