Metrics question
Several large customers say Glean is valuable, but employees still do not trust results to be personalized enough across Microsoft Teams, ServiceNow, and GitHub. How would you diagnose whether the main issue is connector/data quality, permissions, weak user-context signals, ranking, or the UX around results; what would you prioritize first; and what metrics would tell you the experience is actually improving?
- Glean
- Metrics
- Hard
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What this question tests
Tests diagnostic thinking on enterprise search trust, separating data, permissions, ranking, and UX causes, with metrics tied to each.
How to approach it
- Audit connector and data freshness for Teams, ServiceNow, and GitHub, since stale or incomplete indexing is a common root cause of low trust.
- Check permissions mapping accuracy, since results that surface content a user should not see, or hide content they should see, both erode trust fast.
- Review whether ranking uses enough personalized context, like team, role, and recent activity, or defaults to generic relevance.
- Review the UX itself: does the result surface show why a document was returned, which builds trust even when ranking is imperfect.
- Prioritize permissions and data quality fixes first, since a ranking improvement cannot fix results that are wrong or missing.
- Track precision at the top results, click through and dwell time on results, and a direct trust survey score to measure improvement.
What a strong answer includes
- Prioritizes permissions and data quality ahead of ranking, since a well ranked wrong result is worse than a mediocre correct one.
- Names a specific UX fix, showing provenance or why a result was surfaced, as a trust lever independent of ranking quality.
- Proposes both behavioral metrics, click through and dwell time, and a direct trust survey, since usage alone cannot confirm trust.
Common mistakes
- Jumping straight to a ranking algorithm fix without first verifying connector data quality and permissions accuracy.
- Measuring only usage volume, which can stay flat or even rise while user trust in the results is actually falling.
Likely follow-up questions
- How would you distinguish a permissions bug from a genuinely low relevance ranking issue?
- What would you show a user to increase trust in a result without over explaining the system?
More metrics questions
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- You launch new governance and privacy features in Glean Protect. What metrics would you use to determine whether they are actually reducing enterprise AI risk and increasing customer trust, without hurting search/assistant adoption or answer usefulness? Include leading and lagging indicators, and explain how you’d avoid vanity metrics.Glean · Metrics · Hard
- Glean cares about time-to-first-call, integration success rate, and API error rates. Which metrics would you treat as the core indicators that external developers are actually reaching production successfully, which are just supporting diagnostics, and how would you instrument the platform to measure the funnel from initial setup to a live production integration?Glean · Metrics · Medium
- Glean wants customers to safely compare multiple LLMs before committing one to production. What end-user workflow and admin/API capabilities would you prioritize in v1, what would you leave out, and how would you measure whether the experimentation experience is actually helping customers make better rollout decisions?Glean · Metrics · Hard
- You own projections of LLM usage, cost, and capacity planning for a new LLM-native capability. How would you forecast demand at launch, monitor leading indicators after release, and decide when to secure more provider capacity versus routing traffic to alternative models?Glean · Metrics · Hard
More questions from Glean
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop