Product design question
Design the admin and end-user experience for experimenting with different LLMs in Glean while preserving enterprise governance, clear defaults, and user trust in model behavior.
- Glean
- Product design
- Hard
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What this question tests
Tests whether you can design a two sided control surface, admin governance plus end user choice, for switching LLMs inside an enterprise search product.
How to approach it
- Confirm whether model choice happens per workspace, per query, or both.
- Map the admin persona, who needs an allowlist of approved models, data residency rules, and cost caps per workspace.
- Map the end user, who wants a better answer without needing to understand model internals.
- Design an admin console with a small allowlist, say three models, one default, plus logging and spend limits.
- Design an end user picker with plain labels like faster versus more thorough, shown only inside the admin's allowlist.
- Add trust signals: consistent citations regardless of model, a way to flag a bad answer, and an audit trail per model version.
What a strong answer includes
- Keeps admin governance and end user experimentation as two distinct layers, for example an allowlist admins control and a lightweight picker inside it.
- Treats trust as consistency of grounding, for example the same source documents regardless of which model answered, not just raw quality.
- Proposes a default override metric, for example fewer than five percent of users switching away signals a well tuned default.
- Treats cost as a constraint from day one, such as per workspace token budgets, not something added later.
Common mistakes
- Treating this as a simple toggle without addressing SSO scoped policies and audit logging.
- Ignoring how model switching changes citation behavior, which is what actually erodes trust.
Likely follow-up questions
- How would you handle a workspace where the admin never set a policy?
- What signal would tell you the default model you picked is wrong?
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More questions from Glean
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