AI & Technical question

How would you design an evaluation framework to know whether a new Claude model is genuinely better at coding?

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

Tests AI evaluation design: building a framework that measures real coding capability rather than a single benchmark number.

Define what better at coding means concretely: correctness on real tasks, not just benchmark accuracy, including tasks that require multi file changes and tool use.
Build a mixed evaluation suite: standard benchmarks like SWE bench for comparability, plus internal held out tasks that mirror real Claude Code usage.
Include human evaluation for cases benchmarks miss, like code quality, maintainability, and whether a fix actually addresses the root cause versus a symptom.
Add regression testing: confirm the new model does not lose capability on tasks the previous model already handled well.
Track real world signals post launch, like task completion rate and rate of human correction in actual Claude Code sessions.
Confirm with the interviewer whether this evaluation is for an internal go or no go decision, or for a public benchmark claim, since rigor requirements differ.

Combines benchmark scores with real usage telemetry, since a benchmark win does not guarantee developers actually finish tasks faster in practice.
Explicitly checks for regressions, not just gains, since a new model can improve average performance while getting worse at specific common tasks.
Proposes measuring effort, not just correctness, for example number of retries or human interventions needed to reach a working solution.
Distinguishes contamination risk on public benchmarks and proposes held out, private tasks as the more trustworthy signal.

Relying on a single public benchmark number, which can be gamed, contaminated, or unrepresentative of real usage.
Not testing for regressions, so an overall improvement could hide a real capability loss on common tasks.
Ignoring real world signal after launch, treating evaluation as a pre launch only exercise.

How would you weigh benchmark performance against real user telemetry if they disagree?
What would you do if the new model regresses on one important task type?
How would you evaluate coding ability for languages with less benchmark coverage?