Design AI experiment infrastructure.

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ANSWER MODE

WRITTEN ANSWER

### Signal to interviewer

I can design experiment infrastructure that scales learning velocity while protecting decision quality and operational safety.

### Clarify

I would clarify experiment volume, decision owners, acceptable risk during ramp, and baseline metric standards.

### Approach

Build an experiment control tower with registration, cohort assignment, guardrail configuration, rollout controls, and decision dashboards.

### Metrics & instrumentation

Primary metric: cycle time from experiment proposal to actionable decision. Secondary metrics: experiment success rate, analysis rework frequency, and ramp completion reliability. Guardrails: invalid-test launches, guardrail threshold breaches, and delayed rollbacks.

### Tradeoffs

More self-serve capability boosts speed but can reduce methodological rigor. Centralized review improves quality but can become a bottleneck.

### Risks & mitigations

Risk: conflicting metric definitions; mitigate with shared metric registry. Risk: unsafe ramps; mitigate with staged rollout gates. Risk: incorrect causal conclusions; mitigate with automated validity checks.

### Example

For a summarization feature, teams register treatment cohorts, define trust guardrails, and use staged traffic ramps with automated stop conditions.

### 90-second version

Design experiment infrastructure as a control tower: standardize setup, enforce safety checks, and shorten the path from hypothesis to confident decision.

FOLLOW-UPS

Clarification

Which guardrails must be mandatory before any experiment can launch?
How do you standardize metric definitions across product teams?

Depth

How would you implement staged ramps with automatic stop triggers?
What preflight checks catch invalid experiment designs early?

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