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LLM Evals and AI PRDs

Golden Datasets and Test Sets Interview Questions

What a golden dataset is, how big it should be, who owns it and how it leaks into development. A core topic for AI PMs, data scientists and ML engineers.

  • 26 questions with model answers
  • 1 foundational · 9 intermediate · 16 advanced

Foundational Golden Datasets and Test Sets Questions

  1. What is a golden dataset and why does the PM usually own it?Concept

Intermediate Golden Datasets and Test Sets Questions

  1. How do you construct a first golden set with no production traffic?Case
  2. What is a holdout set and when would you use one for an AI product?Concept
  3. Describe a process for adding newly discovered failure cases to the golden set.Case
  4. What governance do you need around who can edit the golden set?Concept
  5. Write the documentation that should accompany a golden set.Artifact critique
  6. Describe the review cadence for a golden dataset.Concept
  7. What happens to your golden set when the product's scope expands?Case
  8. Explain the difference between a golden set, a regression suite and a benchmark.Concept
  9. Critique a golden set built entirely from examples the team wrote themselves.Artifact critique

Advanced Golden Datasets and Test Sets Questions

  1. Describe the composition of a golden set: what proportion should be edge cases?Concept
  2. How do you keep a golden set representative as your user base changes?Case
  3. Explain the risk of a golden set that engineering can see during development.Concept
  4. How do you handle labelling disagreement inside a golden set?Case
  5. How large should a golden set be, and what determines the answer?Calculation
  6. Explain the relationship between the golden set and customer-specific quality complaints.Case
  7. How do you build a golden set for a feature handling sensitive personal data?Case
  8. Describe the tradeoff between a broad golden set and a deep one.Concept
  9. What does it mean for a golden set to go stale, and how do you detect it?Concept
  10. How do you use the golden set during a model migration?Case
  11. Explain how customers could contribute to a golden set without leaking their data.Case
  12. What is the argument for maintaining separate golden sets per customer segment?Concept
  13. How do you prevent the golden set from becoming a proxy for a single loud customer?Case
  14. How would you measure whether your golden set actually predicts production quality?Concept
  15. Who should be able to veto a launch based on golden set results?Concept
  16. Describe how you would build a golden set for a product I name.Interview

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