AI & Technical question
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
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What this question tests
Tests ability to design a multi signal launch gate for Claude Code that reconciles conflicting benchmark and qualitative dogfood evidence.
How to approach it
- Take the conflict seriously rather than picking a side, since strong benchmark gains and worse felt debugging both likely reflect real differences in task distribution.
- Define what each signal measures: benchmark evals for isolated task completion, dogfood feedback for real multi step, ambiguous debugging.
- Add an agentic task suite closer to real debugging, such as multi file bug reproduction, to bridge the gap between the signals.
- Require transcript review on a sample of dogfood sessions to find the specific failure mode, such as premature fixes or poor root cause isolation.
- Weight the qualitative signal heavily for debugging launch readiness specifically, since benchmark gains not showing up in real usage are not ready to ship broadly.
What a strong answer includes
- Treats the conflicting signals as evidence of a benchmark and reality gap rather than assuming one source is simply wrong.
- Proposes a concrete bridging suite, multi step agentic debugging tasks, rather than just trusting the existing benchmark more.
- Names transcript review as the way to find the specific failure mode, not just a vague call for more investigation.
- Sets a hard blocking threshold tied to the qualitative signal, so a benchmark win cannot alone force a launch.
Common mistakes
- Trusting benchmark gains over consistent dogfooder complaints just because the benchmark is quantitative.
- Proposing more evals without ever reviewing actual transcripts to find the real failure mode.
Likely follow-up questions
- What would you do if transcript review found no clear failure pattern?
- How would you decide between a limited rollout and a full delay?
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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