Metrics question
Pick one enterprise workflow where better connector depth, not just more connectors, could materially improve Glean’s assistant or agent outcomes. Explain what product change you would make, how you would launch it to customers, and which success metrics and quality checks you would use to prove it improved real user outcomes.
- Glean
- Metrics
- Hard
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What this question tests
Tests picking one high-leverage workflow, shipping a connector-depth improvement, and proving it improved real outcomes with the right metrics.
How to approach it
- Pick one workflow explicitly, for example ticket-resolution assistance pulling from a specific system like Jira or ServiceNow, rather than speaking generically about 'connectors'.
- Diagnose why shallow connectivity hurts this workflow, for example the assistant retrieves ticket titles but not comment threads, missing the actual resolution context.
- Define the product change: deepen the connector to index comment threads and linked artifacts, not just metadata.
- Launch to a subset of workflows or teams first, comparing answer quality and task completion against a control group still on shallow connectivity.
- Use success metrics tied to the workflow outcome, like ticket-resolution assist accuracy and time-to-resolution, not just query volume.
- Add a quality check, human-rated relevance on a sample of assistant answers, since automated metrics alone can miss subtle context misses.
What a strong answer includes
- Picks one specific workflow and system, not a generic connector-improvement pitch, showing real prioritization.
- Uses a controlled comparison against shallow connectivity to isolate the depth improvement's actual impact.
- Adds a human-rated quality check alongside automated metrics, since retrieval depth issues are easy for pure usage metrics to miss.
Common mistakes
- Speaking about connector depth generically without naming one workflow and system.
- Measuring only query volume instead of task-outcome quality.
Likely follow-up questions
- How would you decide which connector to deepen next after this one?
- What would you do if depth improved answer quality but also increased latency?
More metrics questions
- What metrics prove Glean is delivering value to a large enterprise?Glean · Metrics · Hard
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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