AI & Technical question
A customer reports that Sierra’s agent performs well in English but degrades in Spanish when users use regional slang, register shifts, or code-switching. How would you diagnose whether the issue is prompt design, retrieval/context quality, model limitations, or evaluation gaps, and how would you prioritize fixes with engineering?
- Sierra
- AI & Technical
- Hard
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What this question tests
Tests diagnosing a language-specific quality regression by isolating whether the cause is prompt design, retrieval, model limitations, or evaluation gaps.
How to approach it
- Check evaluation coverage first: if Spanish evals under-sample slang, register shifts, and code-switching, the team may not even be measuring the real failure mode.
- If eval coverage is adequate and still shows degradation, check retrieval and context quality, whether Spanish-language knowledge sources are as complete and current as English ones.
- Check prompt design next, whether prompts and system instructions were translated versus genuinely adapted for Spanish sociolinguistic variation, not literal translation.
- Isolate model limitations last, by testing a fixed prompt and retrieval set across dialect variants to see if the base model's Spanish performance itself is the bottleneck.
- Prioritize expanding eval coverage first, since without it, any other fix is being validated on an incomplete picture.
- Sequence remaining fixes by leverage: retrieval and prompt adaptation are usually cheaper and faster than a model-level fix.
What a strong answer includes
- Starts with evaluation coverage as the first suspect, since a team cannot diagnose a gap it is not measuring accurately.
- Distinguishes translated prompts from genuinely adapted prompts, a specific and often-missed cause of regional-language regressions.
- Sequences fixes by cost and speed, prioritizing retrieval and prompt fixes before assuming a model-level limitation.
Common mistakes
- Assuming a model limitation before ruling out eval gaps or literal-translation prompts.
- Treating all Spanish-speaking users as one uniform population, ignoring regional and register variation.
Likely follow-up questions
- How would you build an eval set that captures code-switching and slang specifically?
- What would you do if the base model itself is the bottleneck?
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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