AI & Technical question
Before launching a GenAI application for a government agency, how would you build the evaluation set, set acceptance thresholds for quality, safety, and reliability, and define the success metrics you would review with the client each week?
- Scale AI
- AI & Technical
- Hard
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What this question tests
Whether you can build a rigorous evaluation and reporting process for a high-stakes government GenAI launch, with real thresholds instead of a vague quality bar.
How to approach it
- Build the evaluation set from real government workflow data, including edge cases and adversarial inputs, reviewed by domain experts from the agency, not just engineers.
- Set acceptance thresholds per dimension separately: quality (task accuracy against the eval set), safety (rate of harmful, biased, or out-of-policy outputs, ideally near zero), and reliability (uptime and consistent latency under real load).
- Require safety to clear a stricter bar than quality, since a government customer will tolerate slower iteration on accuracy far more than any safety incident.
- Define the weekly review metrics: eval pass rate trend, any safety flags raised in production, and open issues with owners and target dates.
- Keep the weekly review structured and consistent, using the same dashboard and metric definitions every week, so the client can track real progress rather than a changing story.
What a strong answer includes
- Sets safety as a stricter, separately-tracked bar than general quality, reflecting real government deployment risk tolerance.
- Specifies who builds and reviews the eval set (domain experts, not just engineers), which matters for legitimacy in a government context.
- Defines a concrete, consistent weekly review structure rather than an ad hoc client update.
Common mistakes
- Uses one blended quality score that hides safety issues inside an average.
- Builds the eval set without involving anyone who understands the actual government workflow.
Likely follow-up questions
- What would you do if a safety flag appears after launch during weekly review.
- How would you keep the eval set current as the agency's workflow evolves.
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture