AI & Technical question
You are preparing a government AI application for deployment, and the customer has strict minimum requirements for accuracy, safety, and reliability. How would you design the evaluation set, choose the right model and product metrics, and use the results to determine whether the team should improve prompts, retrieval, workflow design, training data, or the underlying model before launch?
- Scale AI
- AI & Technical
- Hard
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What this question tests
Whether you can design a rigorous evaluation process for a high-stakes government AI deployment and use the results diagnostically, routing each gap to the right fix instead of a blanket retrain.
How to approach it
- Build the evaluation set from real government task data plus adversarial and edge cases, reviewed by domain experts from the customer, since a generic benchmark will not reflect the agency's actual minimum bar.
- Choose model metrics like task accuracy and calibration, and product metrics like task completion time and human override rate, since both technical quality and workflow fit matter for acceptance.
- Set minimum thresholds per dimension, accuracy, safety, and reliability, agreed with the customer before testing starts, so results are judged against a pre-committed bar, not negotiated after the fact.
- Use failure analysis to route the fix: prompt and retrieval issues show up as correct facts retrieved but poorly synthesized, workflow issues show up as correct answers in the wrong format or sequence, training data gaps show up as consistent errors on a specific topic area.
- Only recommend touching the underlying model if prompt, retrieval, and workflow fixes cannot close the remaining gap, since that is the slowest and most expensive lever.
What a strong answer includes
- Routes each type of eval failure to a specific fix category instead of defaulting to retraining or fine-tuning as the answer.
- Involves agency domain experts in building the eval set, which matters for legitimacy and for catching edge cases engineers would miss.
- Sets pre-committed thresholds with the customer, avoiding a post-hoc negotiation about what counts as good enough.
Common mistakes
- Treats fine-tuning as the default fix for any quality gap instead of diagnosing the failure first.
- Sets thresholds after seeing results, which invites disputes about what good enough means.
Likely follow-up questions
- How would you handle a failure mode that does not clearly map to any single fix category.
- What would you do if the agency's minimum bar is technically unreachable with the current approach.
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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