Start by clarifying requirements and constraints (latency, cost, scale, safety thresholds), then propose a layered architecture with offline evaluation, online monitoring, and automated regression gates. Emphasize trade-offs between automated metrics and human review, and how to handle evolving safety definitions.
Pro tip: Anchor your design around a golden dataset that is versioned and continuously expanded with production edge cases, and propose a canary deployment with automated rollback based on safety and quality metrics. This shows you understand both model lifecycle and operational risk.
Ask about scale (QPS, model size), latency budgets, safety definitions, regulatory requirements, and existing infrastructure. Establish what 'correctness' and 'safety' mean for this team.
Propose a versioned golden dataset with diverse prompts covering correctness, safety, and edge cases. Define automated metrics (e.g., exact match, BLEU, toxicity scores, bias detection) and human-in-the-loop review for ambiguous cases.
Deploy shadow mode and canary releases with real-time metrics (latency, error rates, safety violations). Use statistical tests to compare new vs. baseline models and trigger alerts on regressions.
Define thresholds for promotion (e.g., >95% on golden set, <0.1% safety violations) and automate rollback if canary metrics degrade. Integrate with CI/CD for model deployment.
Continuously mine production data for new edge cases, update golden datasets, and refine metrics. Use A/B testing to measure business impact and adjust safety thresholds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.