Product design question
How would you improve Facebook for commuters?
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What this question tests
Product sense for a defined context, commuting, marked by short bursts of attention and variable connectivity.
How to approach it
- Define the commuter context, short interrupted sessions, variable connectivity like subway tunnels, and often one handed phone use.
- Identify the pain point, feed content and video can be slow to load or interrupted by connectivity loss during a commute.
- Propose a feature, offline ready content pre caching, loading a batch of feed content when the app has strong connectivity, viewable when signal drops.
- Consider format, a condensed, text first feed mode that loads faster and works better with weak connectivity.
- Define success metrics, session completion rate during low connectivity periods and reduction in content failed to load errors.
What a strong answer includes
- Grounds the solution in a real environmental constraint, subway or tunnel connectivity loss, rather than a generic feature idea.
- Proposes a concrete mechanism, pre caching a content buffer during strong signal for offline viewing, similar to how music apps handle offline listening.
- Sets a specific metric tied to the problem, reduction in failed to load errors and improved session completion during detected low connectivity windows.
Common mistakes
- Proposing generic feed improvements with no specific tie to the commuting context.
- Ignoring connectivity as the core constraint and focusing only on content relevance.
- No metric that actually measures the low connectivity experience specifically.
Likely follow-up questions
- How would you detect that a user is currently commuting to trigger this experience?
- How would you handle the trade off between pre caching content and data usage costs?
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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