AI & Technical question
You launch a new artifact-generation capability, and users say the outputs are occasionally excellent but too inconsistent to rely on. How would you define quality, build an evaluation framework across model and product layers, and decide whether to improve, constrain, or roll back the feature?
- Perplexity
- AI & Technical
- Hard
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What this question tests
Tests defining measurable quality for a generative feature and building a layered evaluation framework to decide whether to improve, constrain, or roll back it.
How to approach it
- Define quality operationally: structural correctness (does the artifact render and function), content accuracy, and stylistic consistency, since occasionally excellent likely means one of these varies more than the others.
- Build a model-layer eval: an automated test suite that scores generations against these dimensions on a fixed benchmark set of prompts, run on every model or prompt change.
- Build a product-layer eval: real usage sampling, rated by users or reviewers, and tracked against edit or regeneration rate as a proxy for dissatisfaction.
- Segment inconsistency by prompt type or complexity, since quality may hold for simple artifacts and fail on complex, multi-step ones.
- Set a decision threshold upfront: if the failure rate on high-complexity prompts exceeds an agreed bar, constrain the feature to simpler use cases rather than roll back entirely.
- Decide to improve if failures cluster in a fixable pattern, constrain if failures are concentrated in one segment, or roll back only if failures are broad and unfixable in a reasonable timeframe.
What a strong answer includes
- Separates structural, accuracy, and stylistic quality as distinct axes, since occasionally excellent, occasionally bad likely means inconsistency on one specific axis, not all of them.
- Proposes a model-layer benchmark suite that runs on every change, catching regressions before users do.
- Uses regeneration rate as a practical proxy for dissatisfaction when explicit quality ratings aren't available at scale.
- Sets the improve, constrain, or roll back decision threshold before looking at results, avoiding a post-hoc rationalized call.
Common mistakes
- Treating quality as one undifferentiated score instead of separate structural, accuracy, and style dimensions.
- Deciding to roll back or keep the feature without segmenting where the failures actually concentrate.
- Relying only on user complaints, which undercount silent regeneration or abandonment.
Likely follow-up questions
- What decision would you make if failures are rare but severe when they happen?
- How would you build the benchmark set so it doesn't get gamed by overfitting?
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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