Model Accuracy improves by 20% but Doubles latency. Would you ship it?
AI Product Management Interview Question: You’re given a new model that improves accuracy by 20% but doubles latency. Would you ship it? Walk me through your decision.
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Google, OpenAI, Anthropic Product Management Interview Question:
Model Accuracy improves by 20% but Doubles latency. Would you ship it?
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Framework:
Clarify the Problem and Goals
Identify Stakeholders and User Segments
Tradeoffs with Metrics
Contextual Considerations
Alternatives and Mitigations
Experiment and Rollout plan
Decision Criteria
Checklist, Templates, & Communication plan
Clarify the problem and goals
Purpose
What you are optimizing for: user task success, retention, conversion, revenue, safety, or cost?
Given tradeoff:
Accuracy improves by 20 percent.
Latency doubles (2x slower).
Clarifying Questions:
What does 20 percent accuracy mean in practice? Is it absolute accuracy, relative error reduction, F1, AUC, or something else?
What is the current latency (ms or seconds)? Doubling 50 ms is very different from doubling 2 seconds.
Which downstream metrics truly reflect user value for this product? (Examples: task completion, clicks, purchases, time-on-task.)
Stakeholders & User Segments
Primary stakeholders
Users who consume the model output.
Product managers and Design team.
Data science and ML engineering teams.
Secondary stakeholders
SRE/infra and cost owners.
Legal, compliance, or trust & safety if accuracy or latency affects risk.
User segments:
Power users vs casual users. Power users might tolerate latency for better results.
Real-time users (chat, live search) vs batch users (daily reports, periodic recompute).
High-value transactions (checkout, loan approval) vs low-value interactions (browse suggestions).
