Product design question
How would you improve Google news?
- Product design
- Easy
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What this question tests
Tests prioritization of a concrete improvement for a news aggregation product, tied to a real user pain point around trust and relevance.
How to approach it
- Clarify the user segment: casual users scanning headlines versus users wanting deep coverage of specific topics.
- Identify the pain point: algorithmic news feeds can surface low-quality or biased sources without clear signals of credibility.
- Propose a concrete fix: source credibility and diversity labels shown directly on each story, plus an easy toggle to see multiple perspectives on the same story.
- Prioritize this over generic UI redesigns, since trust and relevance are Google News's core differentiators against social media news feeds.
- Weigh the trade-off: adding credibility signals must avoid appearing to editorialize or pick political sides.
- Define success as increased time spent reading full articles (not just headlines) and user-reported trust in the app.
What a strong answer includes
- Picks a specific, Google-News-relevant pain point, source trust and perspective diversity, instead of a generic app improvement.
- Ties the feature to Google News's actual differentiator against algorithm-driven social feeds, which is aggregation credibility.
- Uses illustrative numbers as assumptions: assume 20% of users report distrust of at least one source shown in their feed.
- Flags the neutrality risk explicitly, which is a real, well-known challenge for any news-ranking product.
Common mistakes
- Proposing generic UI polish disconnected from Google News's core trust and relevance challenge.
- Ignoring the neutrality and bias-perception risk that comes with any credibility labeling feature.
- Not defining a success metric beyond engagement.
Likely follow-up questions
- How would you determine source credibility without appearing biased?
- How would you handle a publisher who disputes their credibility rating?
- What metric would tell you this improved trust, not just engagement?
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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