Metrics question
New safety research can change what should be measured and how. How would you set up a repeatable process for introducing new taxonomies, labels, or eval methods into production metrics while preserving trend comparability, stakeholder trust, and decision speed?
- OpenAI
- Metrics
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests process design for evolving safety measurement over time without breaking trend comparability or stakeholder trust in the numbers.
How to approach it
- Establish a formal intake process for proposed new taxonomies or labels, requiring a clear rationale tied to new safety research findings.
- Run new taxonomies in parallel with the existing ones for a defined period, rather than replacing metrics outright, so trend lines do not break abruptly.
- Document a clear changelog and rationale for every metric definition change, so stakeholders can always trace why a number moved.
- Backfill or re-score historical data under the new taxonomy where feasible, so before and after comparisons remain possible.
- Set a review cadence, for example quarterly, for evaluating new taxonomies rather than reacting ad hoc to every new research finding, to protect decision speed.
- Communicate metric changes proactively to stakeholders before they see a number shift unexpectedly in a report.
What a strong answer includes
- Proposes running old and new taxonomies in parallel specifically to preserve trend comparability, which is the core tension the question raises.
- Includes a documented changelog and rationale, since stakeholder trust depends on being able to explain why a metric moved.
- Sets a defined review cadence rather than reacting to every new research finding immediately, protecting decision speed from constant metric churn.
Common mistakes
- Replacing metrics outright whenever new research emerges, breaking trend lines stakeholders rely on for decisions.
- Having no formal process, so metric changes happen ad hoc and stakeholders lose trust in the numbers over time.
Likely follow-up questions
- How long would you run old and new taxonomies in parallel before fully switching over?
- What would you do if a stakeholder disputes a metric change after it has already shipped?
More metrics questions
- Weekly active users of Codex dropped 15% after a pricing change. How do you investigate?OpenAI · Metrics · Medium
- What metrics would you track to measure the success of ChatGPT Projects?OpenAI · Metrics · Medium
- OpenAI wants one topline safety metric for frontier model deployments. How would you define it so it is credible for leadership decisions, sensitive enough to detect meaningful changes in harm, and decomposable into drivers that research and engineering teams can act on?OpenAI · Metrics · Hard
- This team cares about measurable improvement in defensive outcomes per analyst-hour. For an AI-assisted threat investigation product, what metrics would you use across product quality, operational outcomes, and user behavior? Which would be leading vs. lagging indicators, and how would you handle tradeoffs if adoption is high but investigation accuracy or safety is weak?OpenAI · Metrics · Hard
- What metrics would you use to judge whether a legal AI product is working in a 5-customer pilot versus a scaled rollout? Be specific about user-value, trust/quality, operational, and business metrics, and explain which ones are leading indicators versus launch gates.OpenAI · Metrics · Medium
- What metrics would you use to determine whether the under-18 ChatGPT experience is both helpful and safe? Define a north-star metric, guardrails, and one metric that could be misleading, then explain how those metrics would change your roadmap.OpenAI · Metrics · Hard
More questions from OpenAI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop