AI & Technical question
Tell me about a time you used traces, evals, and user feedback to diagnose an AI or agent product failure in production. What was happening, how did you isolate the root cause across model, prompt, tool, or UX issues, and what product decision did you drive as a result?
- Lovable
- AI & Technical
- Hard
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What this question tests
Whether you have real experience using production telemetry, not just intuition, to root-cause an AI agent failure and drive a concrete product decision.
How to approach it
- Set the scene: what was the observed symptom (for example a spike in failed generations or a specific user complaint pattern) and how you first noticed it.
- Show how you used traces to reconstruct the failing session step by step, rather than guessing from aggregate metrics alone.
- Explain how you separated model issues (bad completion) from prompt issues (missing context), tool issues (a failed or misused tool call), and UX issues (unclear user input or a confusing screen).
- Describe the specific evidence that pointed to the true cause, for example a tool call succeeding but returning data the prompt never asked the model to use correctly.
- State the product decision you drove: a prompt change, a tool-schema fix, a UX clarification, or an escalation to a model or infra fix, and why you chose that lever over the others.
- Close with the measured result: the metric that moved after the fix shipped.
What a strong answer includes
- Uses traces and evals as the actual investigative method, not just 'we looked into it', showing a specific step where trace data ruled out one hypothesis.
- Distinguishes at least three candidate root causes (model, prompt, tool, UX) and shows the evidence that isolated the real one.
- Names the product decision precisely, for example rewriting a tool's error-handling path so the agent could recover instead of silently failing.
- Quantifies the outcome, for example an assumed drop in the failure-rate metric after the fix.
Common mistakes
- Describing the fix without describing the diagnostic process that found the root cause.
- Blaming 'the model' by default without ruling out prompt, tool, or UX causes first.
- No measured outcome to show the fix actually worked.
Likely follow-up questions
- What would you have done if the trace data was inconclusive?
- How did you validate the fix did not introduce a new failure mode?
- How would you build this kind of diagnosis into a repeatable process for the team?
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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