Metrics question
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
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What this question tests
Tests forecasting demand for a new LLM-native capability, monitoring leading indicators post-release, and deciding between securing more provider capacity versus routing to alternative models.
How to approach it
- Forecast launch demand using comparable capability launches, adjusted for the new capability's expected usage intensity, for example if it's used per-query versus per-session, and segment by customer tier likely to adopt fastest.
- Build in a buffer for forecast uncertainty, since new capabilities often see usage spikes that historical comparables underestimate, especially if adoption is viral within an enterprise account.
- After release, monitor leading indicators: request volume growth rate, queue or latency degradation under load, and provider rate-limit utilization, checked more frequently than the launch-week average would suggest necessary.
- Set a clear threshold for action, for example provider utilization crossing a defined percentage of contracted capacity, that triggers a capacity conversation before an actual outage or throttling occurs.
- Decide capacity versus routing based on cost and latency tradeoffs: secure more provider capacity if the capability is core and latency-sensitive, or route overflow traffic to an alternative model if quality degradation from routing is acceptable for that use case.
- Build the routing fallback proactively before it's needed, since building it reactively during a capacity crunch is much riskier and slower.
What a strong answer includes
- Builds explicit uncertainty buffer into the forecast, recognizing new capabilities often exceed comparable-based estimates, especially with viral enterprise adoption.
- Sets a concrete utilization threshold that triggers proactive capacity action, rather than waiting for an actual outage to react.
- Frames capacity versus routing as a genuine cost-latency-quality tradeoff decision, not a default to always buy more capacity.
- Builds the routing fallback proactively before a crunch, since reactive fallback engineering under pressure is much riskier.
Common mistakes
- Forecasting demand without a buffer for the faster-than-expected adoption new capabilities often see.
- Waiting until an actual outage or throttling event to start a capacity conversation instead of a proactive threshold.
- Building a routing fallback only after a capacity crisis has already started, under much higher pressure and risk.
Likely follow-up questions
- How would you decide the acceptable quality tradeoff for routing to an alternative model?
- What would you do if actual demand comes in far below forecast?
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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 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