AI & Technical question
A customer says Sierra’s agent performs well in English but underperforms in German on complex service conversations. How would you isolate whether the problem comes from data coverage, prompting/orchestration, tool use, evaluation gaps, policy behavior, or localization quality? Once you have a hypothesis, how would you prioritize fixes with engineering and explain the plan and tradeoffs to the customer’s executives?
- Sierra
- AI & Technical
- Hard
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What this question tests
Systematic root cause isolation for a localization quality gap, and the ability to translate a technical diagnosis into an executive level explanation and plan.
How to approach it
- Start by confirming the eval itself is fair, checking whether German test scenarios are equally complex and well labeled as the English ones, ruling out an eval gap before assuming a real capability gap.
- Check data coverage next, testing whether the underlying model or fine tuning data has proportionally less German conversational or domain specific data than English.
- Check prompting and orchestration, since prompts, tool descriptions, or system instructions written and tuned primarily in English may not transfer cleanly to German conversational patterns.
- Check tool use and policy behavior separately, since a tool description or policy rule poorly translated or context mismatched could cause failures unrelated to core language capability.
- Prioritize fixes by isolating the single largest contributor first, for example if a controlled test shows translating prompts to German closes most of the gap, prioritize prompt localization over a deeper data investment.
- Explain to executives in plain terms: name the root cause found, the fix being prioritized, the expected timeline, and the interim mitigation, such as flagging complex German conversations for closer human review.
What a strong answer includes
- Runs the diagnosis in a disciplined order, ruling out eval and prompting issues before concluding it is a deeper data or model gap.
- Proposes a specific controlled test, like fully localizing prompts, to isolate which factor matters most.
- Gives executives a plain language explanation plus an interim mitigation, not just a technical postmortem.
- Prioritizes the fix by evidence of actual impact rather than by which fix is easiest to explain.
Common mistakes
- Assuming underlying model capability is the cause without first checking prompting, tooling, and eval design.
- Presenting only technical detail to executives without a plain language root cause and mitigation plan.
Likely follow-up questions
- What interim step would you offer the customer while the real fix is in progress?
- How would you prevent this same gap from appearing in a third language later?
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More questions from Sierra
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