An evals conversation asks you to connect evidence to a product decision. A model score alone cannot tell you whether to ship: you need the sample size, the failures behind the average and the consequences of those failures. Practise making that connection before presenting a rollout plan.
The example below is our fictional rehearsal. It develops product judgment without claiming that evaluation carries more weight than every other interview topic. Use the recruiter's guidance to allocate your preparation time.
Why quality became the PM's problem
Even deterministic products require PM judgment about quality, accessibility and acceptable failure. Generative systems add variable outputs that cannot be fully specified before testing. The product owner must decide which failures are tolerable and which prevent release.
A model-backed feature has no fully specified behaviour. It is right some fraction of the time, and somebody has to decide which fraction is acceptable, on which inputs, measured how. Those are product decisions. Delegating them means delegating the product.
A concrete role signal is OpenAI's Core Models PM posting, reviewed in October 2026. It connects user feedback, evaluations and model release work. That supports practising evaluation decisions; it does not disclose the company's interview questions or scoring weights.
A sequence for rehearsing the decision
Use the following sequence to examine different parts of your answer. It is a practice structure, not a reported interview script.
The first is what good means. The interviewer wants a definition specific enough that two people grading the same output would agree. "Helpful" fails that test. "States the correct refund amount and deadline, and promises nothing the policy does not allow" passes it.
Next comes the set: how many examples, and which ones. A few hundred cases drawn from real traffic and weighted toward the hard and risky ones beat thousands of easy ones. Sharper candidates also say how the set grows, adding every production failure that gets fixed so it cannot come back unnoticed.
Then grading. People first, and a model judge only once it has been checked against those people. A useful follow-up asks how you know the judge is right.
After that, the launch bar. An overall pass rate is not enough on its own. Strong answers add a floor for the weakest segment that matters and a short list of failures that block launch whatever the average says.
Last, life after launch. Production traffic drifts away from any test set, so the answer needs weekly sampling, the online signals that have predicted quality before, and a rerun on every prompt change and every provider model update.
A worked exchange
Use this original practice prompt: "Your contract-summary assistant scores 88% on its eval set. Legal wants it live next week. What do you do?"
A weak answer congratulates the team, proposes a beta and promises to monitor feedback. It proposes exposure before establishing whether the errors make that exposure acceptable.
A strong answer starts with two questions. How large is the set? Suppose it is 150 contracts. At 88%, the margin on 150 cases is a little over five points either way, so the true rate could plausibly sit anywhere from the low 80s to the low 90s. Then: what are the 18 failures? Reading them is the job. If six of them misstate a termination or renewal date, the headline rate stops mattering, because a wrong date in a contract summary is the failure that causes real harm and it is happening in a third of the misses.
Read diagram description
An 88 percent pass rate on 150 contracts, 132 passing, has a 95 percent Wilson interval of about 81.8 to 92.3 percent, so the true rate could be anywhere from the low 80s to the low 90s. For comparison, the same 88 percent on 1,000 contracts would narrow the interval to about 85.8 to 89.9 percent. The failures still need reading before a decision.
Now the candidate can decide. Ship to internal lawyers rather than to clients. Require a confirmation step on any clause with a date in it. Add a hundred contracts weighted toward date-heavy clauses to the set, and set the launch gate before seeing the new results: 90% overall, no contract type under 80%, and zero misstated dates on the date subset. Track how often lawyers edit the summaries in the first month, because heavy editing means the assistant is generating work rather than saving it.
That answer took a position, used arithmetic, read the failures, and still gave Legal something next week. It is roughly what a Strong Hire line in a rubric describes.
Four mistakes that cost the round
Accuracy with no definition. "We would track accuracy" invites the obvious follow-up: accuracy of what, judged by whom? Define the pass rule before naming any metric.
One number, no segments. An assistant at 92% overall can sit at 60% on queries in Hindi or on the longest documents. Name the segment you would check first, and why.
Trusting the judge by default. A model grading another model is a second source of error. Say how you would measure agreement with human labels, especially on failures.
Setting the bar after the results. A bar chosen once the numbers are in tends to land just below whatever the team achieved. Write the gate down first.
A week of preparation
Spend one evening grading real outputs by hand, for a product you use or one you build in an afternoon. The point is to feel how quickly vague quality turns into specific failure types.
Spend the next on the arithmetic: what a pass rate on 100, 200 and 1,000 cases can tell you, and when pairing the same cases can improve precision, and why the disagreement counts matter.
Then practise out loud. Take three evals questions, write your answer before reading any model answer, and grade yourself against a rubric with Weak, Hire and Strong Hire lines. The gap between what you meant and what you said is where the remaining preparation belongs.
Questions about this topic
A prompt that asks how you would know a model-backed feature is good enough to ship, or hands you evaluation results and asks for a launch call. It tests whether you can turn quality into a number, defend the number, and say what would change it.