AI & Technical question
Researchers say Claude Science is useful for workflows like protein structure analysis and chemistry research, but not consistently trustworthy. How would you define target model behaviors, build workflow-grounded evals with research and engineering, surface the highest-risk failure modes, and set a clear launch-readiness bar for broader rollout?
- Anthropic
- AI & Technical
- Hard
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What this question tests
Tests ability to build workflow grounded evals and a launch readiness bar for a scientific AI product where trust, not raw capability, is the blocker.
How to approach it
- Define target behaviors concretely for a specific workflow, such as protein structure analysis, in terms researchers judge trust by, like citation accuracy.
- Build evals grounded in real research tasks with domain experts, not generic benchmarks, so pass rates reflect actual scientific validity.
- Surface the highest risk failure modes through structured expert review, such as confidently wrong claims about an interaction that look plausible but are false.
- Distinguish failure modes by consequence, treating a confidently wrong claim as higher risk than an obviously incomplete answer the researcher will catch.
- Set a launch bar tied to the highest risk failure mode, a strict ceiling on confidently wrong claims even if usefulness scores are high.
What a strong answer includes
- Builds evals with domain experts on real research tasks instead of relying on generic accuracy benchmarks that miss scientific nuance.
- Separates failure modes by consequence, prioritizing confidently wrong claims over obviously incomplete ones since the former are harder for researchers to catch.
- Sets the launch bar around the riskiest failure mode specifically, not an aggregate quality score that could mask a dangerous outlier.
- Treats trustworthiness as a distinct dimension from usefulness, since a model can be useful yet still not consistently trustworthy.
Common mistakes
- Using generic accuracy benchmarks instead of workflow grounded evals built with actual domain experts.
- Setting a launch bar on average quality that lets a dangerous confidently wrong failure mode slip through.
Likely follow-up questions
- How would you recruit and calibrate the domain experts scoring these evals?
- What would you do if the highest risk failure mode was rare but severe?
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- Researchers deliver a model that is materially better at code generation in lab evals, but developer success rates inside Claude Code do not improve. How would you diagnose whether the gap comes from prompting, tool use, context management, latency or reliability, or the eval itself, and what changes would you make to convert model gains into user-visible outcomes?Anthropic · AI & Technical · Hard
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- For a consequential agency workflow like benefits claims review or financial misconduct analysis, what evaluation framework would you put in place before expanding deployment of Claude? Describe the offline and in-production metrics, human-review thresholds, and launch gates you would use to judge whether the model is safe and useful enough for broader use.Anthropic · AI & Technical · Hard
More questions from Anthropic
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