AI & Technical question
A customer says, 'Glean answers questions well, but we don’t trust it to take actions because it misses too much enterprise context.' How would you diagnose whether the root cause is connector coverage, stale data, permissions, graph quality, retrieval/ranking, or action guardrails, and what product changes would you make first?
- Glean
- AI & Technical
- Hard
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What this question tests
Whether you can systematically root-cause a trust complaint in an enterprise AI system across data, retrieval, and guardrail layers instead of guessing at one cause.
How to approach it
- Restate the complaint precisely: the customer trusts answers but not actions, which suggests the gap is specifically about confidence in taking consequential steps, not raw retrieval quality.
- Check connector coverage and freshness first, since missing or stale enterprise context is the most common and most fixable cause of 'misses context'.
- Check permissions next, since content the system cannot see due to access controls will look identical to a retrieval failure from the user's side.
- Audit retrieval and ranking quality on a sample of real failed queries to see if relevant content exists but is not surfaced.
- Separately evaluate action guardrails: even with perfect context, the action layer might be intentionally conservative, which the customer may be reading as 'misses context' rather than 'is cautious by design'.
- Prioritize fixes by which layer explains the most observed failures in the sample, starting with connector coverage and permissions since those are usually the highest-leverage, most tractable fixes.
What a strong answer includes
- Separates the customer's stated complaint (missing context) from the range of technical causes it could map to, showing real diagnostic discipline.
- Notes that permission gaps can masquerade as retrieval failures, which is a genuine, specific insight about enterprise search systems.
- Proposes sampling real failed queries as the validation method, not guessing top-down.
- Distinguishes a guardrail being cautious by design from a genuine context gap, since conflating them would lead to the wrong fix.
Common mistakes
- Jumping straight to 'improve the model' without checking connector coverage, freshness, and permissions first.
- Not distinguishing a deliberate action guardrail from an actual data quality problem.
- No plan to validate the diagnosis against real failed queries before proposing a fix.
Likely follow-up questions
- How would you sample and review the failed queries at scale?
- What would you do if the root cause is permissions, which the customer's own IT team controls?
- How would you communicate a guardrail-driven limitation to the customer without sounding defensive?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture