This is one of those questions that sounds contained until you realize it's actually six questions stitched together.
Start by framing the router as a classification system with a clear objective: maximize user satisfaction while respecting Siri's privacy and on-device strengths versus GPT's generative capabilities. Walk through the full lifecycle—from defining product metrics and labeling ground truth to model design, ambiguity handling, offline evaluation, and online experimentation—emphasizing trade-offs and Apple-specific constraints like privacy and latency.
Pro tip: Anchor your answer in Apple's privacy-first philosophy: propose on-device routing with federated learning or differential privacy for sensitive queries, and highlight how this differentiates from cloud-only competitors. Also, mention that you'd start with a simple heuristic baseline (e.g., keyword-based) before investing in complex models, to validate the problem and gather data quickly.
Identify north-star metrics (e.g., task success rate, user satisfaction, retention) and guardrail metrics (latency, privacy compliance). Establish a labeling schema for ground truth: have human raters annotate which assistant is better for each query, and use implicit signals (e.g., follow-up queries, abandonment) to augment labels.
Propose a two-stage system: first, a lightweight on-device classifier (e.g., logistic regression or small BERT) to handle clear cases and filter sensitive queries; second, a more complex model (e.g., fine-tuned LLM) for ambiguous cases, potentially on-device or via private cloud. Include features like query intent, entity types, and user context.
For ambiguous queries, design the router to either ask a clarifying question or default to the safer assistant (e.g., Siri for privacy-sensitive). For multi-intent queries, consider decomposing into sub-queries and routing each part, or using a confidence threshold to decide if a single assistant can handle all intents.
Build a held-out test set with human labels and evaluate using metrics like accuracy, F1, and calibration. Simulate user interactions to estimate task success. Analyze errors to refine features and model, and ensure fairness across query types and user demographics.
Run an A/B test with a control (current routing) and treatment (new router), randomizing at user level. Measure primary metrics (task success, satisfaction) and guardrails (latency, privacy incidents). Use sequential testing to monitor for early wins or harms, and plan for long-term holdback to assess retention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.