AI & Technical question
Glean Model Hub lets enterprise customers choose among LLMs for search, assistant, and agent workflows. How would you build a model-provider evaluation framework to decide which new providers to add, including the gating criteria, offline/online evals, and the tradeoffs you would make across answer quality, latency, security, cost, and enterprise-specific requirements?
- Glean
- AI & Technical
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests building a model-provider evaluation framework for a multi-LLM enterprise product, defining gating criteria and the offline and online evaluation process across quality, latency, security, and cost.
How to approach it
- Set gating criteria before evaluating any candidate: minimum security and compliance certifications, a defined maximum latency at expected load, and a floor on answer-quality benchmarks relative to already-supported models.
- Run offline evals first: a fixed benchmark set covering search relevance, assistant answer accuracy, and tool-use reliability, scored consistently across all candidate providers.
- Run online evals for providers that pass offline gates: a limited internal or design-partner rollout comparing real usage quality, latency, and cost against existing supported models.
- Weigh cost against quality explicitly, since a marginally better model at meaningfully higher cost may not clear the bar for a broad enterprise rollout, but could still be added as an opt-in premium option.
- Weigh enterprise-specific requirements, like data residency or provider-side data retention policies, as hard gates for regulated customer segments, separate from general quality scoring.
- Decide to add a provider only when it clears security gates, meets the quality floor in both offline and online evals, and has a clear cost or capability differentiation from what's already supported.
What a strong answer includes
- Separates hard security and compliance gates from quality tradeoff scoring, so a high-quality model still can't bypass compliance requirements.
- Requires both offline and online evaluation stages, catching gaps that a static benchmark alone would miss once real usage patterns are involved.
- Treats cost versus quality as an explicit tradeoff that can lead to an opt-in premium tier rather than a binary add or reject decision.
- Names data residency and retention as enterprise-specific hard gates distinct from general quality, appropriate for regulated customer segments.
Common mistakes
- Evaluating quality alone without hard security or compliance gates specific to enterprise and regulated customers.
- Skipping online evaluation and relying only on offline benchmarks, missing real-world usage failure modes.
- Treating every quality improvement as worth adding regardless of cost or genuine differentiation from existing providers.
Likely follow-up questions
- How would you handle a provider that's excellent on quality but weak on data residency guarantees?
- What would make you remove a previously approved provider later?
More ai & technical questions
- How would you measure whether Glean's AI answers are accurate and well-cited?Glean · AI & Technical · Medium
- How would you design a governance model that behaves consistently across Glean's UI, public APIs, and heterogeneous enterprise connectors, for example, a shared policy layer for permissions, action scopes, audit logging, and redaction, even when source systems expose different authentication models and data granularity?Glean · AI & Technical · Hard
- A Fortune 500 customer asks for an audit trail for every AI-agent answer: which data sources were accessed, which permissions were applied, why the response was generated, and whether sensitive content was filtered or redacted. Design the admin and end-user experience, and the core APIs/data model needed to support this without overwhelming either audience.Glean · AI & Technical · Hard
- Several enterprise customers report that the ServiceNow and GitHub connectors are ingesting data successfully, but search results and assistant answers still feel incomplete or low quality. How would you diagnose where the breakdown is across crawl freshness, permissions sync, metadata extraction, entity mapping, ranking, and UX, and how would you decide which fixes to ship first?Glean · AI & Technical · Hard
- Design the next version of Glean’s Indexing API and custom connector platform for large enterprises that need to bring proprietary data sources online quickly. What core capabilities would you prioritize, such as schema/modeling, auth and permissions, incremental sync, observability, and governance, and what principles would you use to keep the platform flexible without creating an inconsistent product experience?Glean · AI & Technical · Hard
- Suppose Glean wants to launch a new answer-generation experience in core search. How would you define the MVP and rollout plan given enterprise constraints around permissions, citations, latency, model choice, and admin controls? What would need to be true before you scaled it broadly?Glean · AI & Technical · Hard
More questions from Glean
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