Strategy question
How would you decide whether to build a feature or fix a bug (assuming both take the same engineering effort)?
- Meta
- Strategy
- Easy
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What this question tests
Tests prioritization judgment: building a decision framework for feature versus bug when effort is held equal, naming what actually differentiates the two.
How to approach it
- Note effort is equal, so the call rests on impact and risk alone.
- Compare on three axes: user impact (how many, how severely), business impact (revenue, trust, retention), and urgency/reversibility.
- Size the bug: how many users hit it, whether it blocks a core action like posting or checkout, and whether it's worsening.
- Size the feature: expected impact from data (competitive gap, user requests, an engagement estimate) and time-sensitivity of the opportunity.
- Apply a default rule: a bug breaking a core flow or trust usually beats a feature, since it actively subtracts value.
- If roughly equal, break the tie with compounding risk: unfixed bugs often worsen, delayed features usually don't.
What a strong answer includes
- Names concrete comparison axes instead of answering 'it depends' with no structure.
- States a clear default rule while acknowledging exceptions, showing decisive but nuanced judgment.
- Introduces compounding risk as a genuinely useful tie-breaker beyond simple severity comparison.
- Keeps the framework general enough to apply to any bug/feature pair, not one narrow example.
Common mistakes
- A flat rule ('always fix bugs first') with no reasoning about severity or context.
- A vague answer that never commits to an actual framework.
- Re-litigating effort trade-offs the question already ruled out as equal.
Likely follow-up questions
- What if the bug affects 0.1% of users but the feature drives major revenue?
- How would you explain this trade-off to a frustrated engineering team?
- How does the framework change under a hard external deadline?
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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 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop