Product design question
Users can generate an app quickly with AI, but many struggle to make precise edits to layout, typography, and theming afterward. How would you identify which visual editing workflows to improve first, and what would the first shippable slice be?
- Lovable
- Product design
- Hard
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What this question tests
Tests identifying which precise-edit workflows matter most after AI generation, and scoping the smallest shippable slice that addresses the sharpest pain.
How to approach it
- Segment the post-generation edit journey: layout adjustments, typography changes, and theming or color changes, and find which one users attempt most and abandon most.
- Use product analytics, edit attempts, undo rate, and time spent per edit type, plus support tickets, to rank the workflows by frequency and friction.
- Interview a handful of users who abandoned an edit to understand whether the blocker is discoverability of the right control or the control being too imprecise.
- Pick the workflow with the highest frequency and clearest fix, likely layout adjustment since it's attempted most often across app types.
- Scope the first shippable slice narrowly, for example a direct-manipulation resize and reposition control for the most common layout elements, not a full design-system editor.
- Ship and measure whether edit completion rate and abandonment for that workflow improve before moving to typography or theming.
What a strong answer includes
- Uses concrete signals, undo rate and abandonment, to rank workflows instead of guessing which feels most important.
- Distinguishes a discoverability problem from a precision problem, since they need different fixes.
- Scopes the first release narrowly to the highest-frequency element type rather than building a full editor upfront.
- Defines a measurable success check, edit completion rate, before expanding to the next workflow.
Common mistakes
- Trying to fix layout, typography, and theming all in one release instead of shipping the narrowest slice first.
- Assuming low usage of an edit type means low value, without checking if it's a discoverability problem.
- Skipping a re-measurement step after shipping to confirm the fix actually worked.
Likely follow-up questions
- How would you tell a discoverability problem from a precision problem in the data?
- What would the second shippable slice be after this one?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop