Product design question
How can the service of Facebook Messenger be improved?
- Meta
- Product design
- Easy
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What this question tests
Tests product sense for a mature, high-usage messaging product, picking one specific friction point over generic feature additions.
How to approach it
- Clarify scope: assume the core one-to-one and group chat experience for everyday users, not the business messaging surface.
- Segment users: heavy daily chatters, and users who mainly use it for occasional group coordination.
- Pick one pain point: managing message overload in busy group chats, missing important messages amid casual chatter.
- Propose a lightweight important flag or smart-thread summary surfacing messages needing a response, without requiring users to mute or leave groups.
- Address the trade-off: AI-based summarization must be transparent and dismissible, since users may distrust an algorithm judging personal conversations.
- Define success: reduction in reported missed-message complaints, and engagement with the summary feature in high-volume group chats.
What a strong answer includes
- Picks a specific, common pain point, group chat overload, rather than a broad list of feature requests.
- Proposes a concrete, opt-in mechanism, a smart summary, rather than forcing a new default behavior on all chats.
- Addresses trust explicitly, since summarizing personal conversations carries more sensitivity than a work inbox.
- Defines success with both a complaint-reduction metric and a feature-adoption metric.
Common mistakes
- Suggesting broad, unrelated feature additions instead of solving a real usage friction.
- Ignoring the trust and privacy sensitivity of AI summarizing personal messages.
Likely follow-up questions
- How would you test this without users feeling surveilled in their personal chats?
- How would this differ for large group chats versus close friend threads?
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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