Public benchmarks score the model. Your product is the model plus prompt, retrieval and tools. It also parses output and runs on your users' real inputs. A high score does not transfer.
Three concrete gaps:
Distribution mismatch. MMLU is multiple-choice academic questions across 57 subjects. HumanEval is 164 small, self-contained Python functions. Neither looks like your support tickets or your codebase.
Contamination and saturation. Benchmark items leak into training data, so scores flatter the model. Strong models now cluster near the ceiling, and the gaps between them sit inside the noise.
Wrong target. They say nothing about format compliance, tone or refusal behavior. Nor do they measure latency or cost per request.
Use them to shortlist two or three candidates. Then decide with your own eval set on your own traffic.
Rewriting in plainer words…
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How would you check whether a model's benchmark score is inflated by contamination?Compare scores on the original items with paraphrased or post-cutoff items. A sharp drop, or verbatim recall of items, points to leakage.
Two shortlisted models score within a point of each other on your eval. How do you choose?Check whether the gap beats run-to-run noise with repeats and enough samples, then decide on cost, latency and format compliance.
A new model version tops the leaderboards. What do you do before switching your product to it?Run your regression set on it, compare quality, format, latency and cost per request, then shadow or canary it on real traffic before full rollout.
Public benchmarks score the model on its own, but my product is the model plus the prompt, retrieval, tools and output parsing, all running on my users' real inputs.
The first gap is distribution. MMLU is multiple-choice academic questions across 57 subjects, HumanEval is 164 small Python functions, and neither looks like my support tickets.
Second, contamination and saturation. Test items leak into training data, and strong models now cluster near the ceiling, so the gaps between them sit inside the noise.
Third, they measure the wrong target. They say nothing about format compliance, tone or refusal behavior, and nothing about latency or cost per request.
So I use them to cut the field to two or three candidates, then make the real decision with my own eval set on my own traffic.
Weak answers to avoid
Says benchmarks are uselessThey are good for a shortlist. Dismissing them outright sounds dogmatic, so say where they help and where your own evals take over.
Only cites contaminationContamination is one gap. Even a clean benchmark would not look like your inputs or measure format, tone, latency or cost.
Picks the top-ranked model on a small leadNear the ceiling, small gaps sit inside the noise. Say you would test the top few on your own eval set before choosing.
Explains the problem, never the fixThe interviewer wants to hear what you would do instead, which is an eval set drawn from real traffic and scored on what your product needs.
Interview lens
Likely follow-ups, what you can say, and the weak answers to avoid.
