AI & Technical question
A new Claude capability unlocks major user value but creates a rare, high-severity misuse risk. How would you design the detection, evaluation, and intervention system to reduce that risk while minimizing false positives on legitimate use cases?
- Anthropic
- AI & Technical
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests building an evaluation and intervention system for a low-frequency, high-severity risk, where naive approaches either miss the harm or over-flag normal users.
How to approach it
- Characterize the rare risk concretely: what a true positive looks like and how rare it's expected to be in production traffic.
- Build a labeled evaluation set combining synthetic and red-team cases with any real historical cases available, since positives are rare.
- Design detection to favor recall on this narrow category using layered signals rather than one classifier alone.
- Add a human-review step for anything flagged, since rarity makes full automation both risky and unnecessary at current volume.
- Track false positives closely, since even a small false-positive rate can dominate total flags when true positives are scarce.
What a strong answer includes
- Explicitly addresses the base-rate problem, where a high-precision-looking system can still produce mostly false positives in absolute terms.
- Combines synthetic and red-team data with real cases to build the eval set, since real positive examples will be scarce.
- Adds a human-in-the-loop review layer given the low volume, rather than fully automating enforcement.
- Names how they'd catch a missed case, such as periodic sampling of un-flagged traffic.
Common mistakes
- Ignoring the base-rate problem and reporting precision without discussing what it means at rare-event volumes.
- Fully automating enforcement on a rare, high-severity risk without human review.
Likely follow-up questions
- How would you build an evaluation set when true positives are extremely rare?
- What would you do if the false-positive rate turned out higher than expected after launch?
More ai & technical questions
- How would you reduce over-cautious refusals without compromising safety?Anthropic · AI & Technical · Hard
- How would you design an evaluation framework to know whether a new Claude model is genuinely better at coding?Anthropic · AI & Technical · Hard
- Offline evals show strong SWE-bench-style gains, but internal dogfooders say the model feels worse on real debugging workflows. Design a launch-gating framework for Claude Code that combines benchmark evals, agentic task suites, transcript review, and limited-rollout criteria. What would you measure, how would you weight conflicting signals, and what thresholds would block launch?Anthropic · AI & Technical · Hard
- 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
- Across many agentic coding tasks, Claude Code shows a recurring failure mode like looping, weak planning, or bad tool selection. How would you isolate whether the issue is in the base model, prompting, tool interfaces, or task decomposition, and what reusable infrastructure would you build to catch and prevent this class of regressions?Anthropic · AI & Technical · Hard
- 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
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