Start by framing the problem as a reliability and trust challenge, then propose a modular architecture with a decision layer that routes queries based on confidence, knowledge boundaries, and risk. Walk through the decision logic (answer, clarify, retrieve, refuse) and finish with concrete metrics for offline and online evaluation, emphasizing trade-offs between helpfulness and safety.
Pro tip: Show that you understand OpenAI's iterative deployment philosophy: design for graceful degradation and continuous learning from user feedback, rather than trying to solve uncertainty perfectly upfront.
Define what 'doesn't know' means (e.g., out-of-distribution, ambiguous, or high-risk queries) and the system's goals: maximize helpfulness while minimizing hallucinations and harmful outputs. Consider latency, cost, and user experience constraints.
Outline components: a query understanding module, a knowledge/confidence estimator, a retrieval system, a decision router, and a response generator. Emphasize separation of concerns and fallback mechanisms.
Explain how the system decides among answer, clarify, retrieve, or refuse. Use signals like model confidence scores, uncertainty quantification, retrieval relevance, and safety classifiers. Describe thresholds and how they are tuned.
For each action, describe the implementation: e.g., for clarify, generate targeted questions; for retrieve, use RAG with source attribution; for refuse, provide safe fallback and suggest alternatives. Include how to handle partial knowledge.
Propose offline metrics (e.g., hallucination rate, refusal accuracy, retrieval precision) and online metrics (e.g., user satisfaction, task success, engagement). Describe A/B testing and feedback loops for continuous improvement.
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