AI & Technical question
Lovable wants this PM to audit the quality of AI-generated visual output. How would you evaluate whether generated designs are consistent, on-brand, and professional at scale, and then translate the biggest quality gaps into a prioritized product roadmap?
- Lovable
- AI & Technical
- Hard
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What this question tests
Tests building an evaluation approach for subjective, generative visual output at scale, then translating quality gaps into a prioritized, buildable roadmap.
How to approach it
- Define what on-brand and professional mean operationally: consistent spacing and typography scale, color contrast that meets accessibility thresholds, and layout patterns that match common design conventions.
- Build a scoring rubric with both automated checks, contrast ratios, spacing consistency, and human review, a sample of generations rated by design-literate reviewers on a fixed scale.
- Sample generations across app categories, since visual quality gaps likely differ between a marketing landing page and a data-heavy dashboard.
- Rank the biggest quality gaps by frequency and by how much they hurt perceived professionalism, for example inconsistent spacing may occur more often than bad color choices.
- Translate the top two or three gaps into scoped roadmap items, for example a layout-consistency pass before a full theming system, since it likely affects more generations.
- Set a recurring cadence to re-run the rubric after each fix ships, to confirm the gap actually closed.
What a strong answer includes
- Combines automated checks, like contrast ratios, with human rubric scoring, since visual quality is only partly measurable automatically.
- Segments evaluation by app category, catching that dashboards and landing pages likely fail differently.
- Prioritizes fixes by frequency times perceived-quality impact, not just by which gap is easiest to fix.
- Closes the loop by re-scoring after each fix ships, rather than assuming the roadmap item solved it.
Common mistakes
- Relying only on human spot-checks without any repeatable, comparable scoring method.
- Treating all generated apps as one category when quality issues likely vary by app type.
- Prioritizing the most visually dramatic bug over the most frequent, cumulative one.
Likely follow-up questions
- How would you keep the rubric consistent as reviewers change over time?
- Which single quality gap would you fix first, and why?
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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