AI & Technical question
An enterprise agent handling millions of support chats has a flat resolution rate, but escalations to human agents are up and customer trust signals are down. Walk me through how you'd diagnose whether the problem comes from model behavior, retrieval or knowledge quality, workflow or routing changes, backend integration failures, or policy changes; what data and evals you'd pull first; how you'd prioritize fixes; and what you'd ship in the next 2 weeks versus longer term.
- Sierra
- AI & Technical
- Hard
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What this question tests
Tests rapid, structured diagnosis of a flat-resolution, rising-escalation problem at scale, and prioritizing fixes across a short and a longer horizon.
How to approach it
- Pull data first: resolution rate and escalation rate trend over time, segmented by intent, to see if the flat trend is uniform or concentrated in specific intents.
- Check for recent changes in each candidate cause: model version updates, knowledge-base changes, workflow or routing changes, and backend integration incident logs, in that order of likely recency.
- Run offline evals on the current model against a fixed benchmark to see if model behavior itself has regressed versus other factors shifting around a stable model.
- Cross-reference trust signals, like CSAT and repeat-contact rate, with the escalation data to see if the two-week 'ship now' fix should target trust or capability.
- For the next two weeks, ship the highest-confidence, lowest-risk fix, likely a knowledge-base or routing correction if one was recently changed.
- For the longer term, address deeper causes like retrieval quality or backend integration reliability that require more testing before a safe rollout.
What a strong answer includes
- Checks recent changes across all four causes in order of likely recency rather than guessing at one cause first.
- Uses offline evals against a fixed benchmark to isolate whether the model itself regressed, a concrete diagnostic step.
- Splits the response into a fast, low-risk two-week fix and a longer-term structural fix, rather than one big remediation plan.
Common mistakes
- Assuming model regression without checking for recent workflow or knowledge-base changes first.
- Proposing only a long-term fix with nothing shippable in the next two weeks.
Likely follow-up questions
- How would you validate the two-week fix actually worked before committing to the longer-term plan?
- What would you do if multiple causes turn out to be compounding at once?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Making AI Product Decisions
- Chapter 8: Designing Evals for AI Products
- Chapter 6: Designing AI Agents and Tooling