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AI Product Metrics and LLM Cost

AI Quality Metrics: Accuracy, Usefulness, Trust Interview Questions

Why an accurate answer can still be useless, what automation bias looks like in the numbers, and how to measure trust. Relevant to AI PMs, data scientists and analysts.

  • 25 questions with model answers
  • 2 foundational · 7 intermediate · 16 advanced

Foundational AI Quality Metrics: Accuracy, Usefulness, Trust Questions

  1. Define accuracy, usefulness and trust as three distinct measurable properties.Concept
  2. Give an example of an output that is accurate but not useful.Concept

Intermediate AI Quality Metrics: Accuracy, Usefulness, Trust Questions

  1. Give an example of a product that is useful despite being frequently wrong.Concept
  2. Design a survey instrument to measure perceived reliability.Artifact critique
  3. Explain how a single high-profile failure affects trust disproportionately.Concept
  4. What is the relationship between latency and perceived quality?Concept
  5. Explain the difference between helpfulness and harmlessness as product properties.Concept
  6. Critique a quality report that presents a single accuracy number.Artifact critique
  7. What is the product implication of a model that fails confidently?Concept

Advanced AI Quality Metrics: Accuracy, Usefulness, Trust Questions

  1. How would you measure trust in an AI feature?Case
  2. Explain why improving accuracy can decrease trust.Concept
  3. Describe the calibration problem: what happens when confidence does not match correctness?Concept
  4. How do you measure whether users over-trust your AI feature?Case
  5. What is automation bias and what product metric would surface it?Concept
  6. How do citations affect measured trust, and does that hold if the citations are wrong?Case
  7. Describe how you would measure usefulness for a feature with no obvious ground truth.Case
  8. How would you decide between a model that is right 90 percent of the time and one that is right 85 percent but says when it is unsure?Case
  9. What metric captures a model's willingness to say it does not know?Concept
  10. Describe how you would measure quality for a creative generation feature.Case
  11. Explain how quality perception differs between novice and expert users.Concept
  12. How does the severity distribution of errors matter more than the error rate?Concept
  13. How do you build a trust recovery plan after a public quality incident?Case
  14. Describe the metric you would use to compare two models on the same task from a user's point of view.Concept
  15. Explain when you would deliberately reduce accuracy to increase usefulness.Case
  16. Argue that trust is the only quality metric that matters, then argue against it.Interview

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