System Design: Ranking Candidate Text Responses to Maximize User Satisfaction

You are designing an end-to-end machine learning system that, given a user query (possibly multi-turn context), ranks multiple candidate text responses and selects the best one to maximize user satisfaction.

Specify and justify the following:

1. Problem formulation and objective
   • Define the prediction task and training objective.
   • Identify labels or proxies for user satisfaction.
2. Data sources and labeling strategy
   • Implicit feedback (e.g., clicks, dwell, conversation continuation).
   • Explicit human ratings or preference labels.
   • How to handle bias and noise in logs.
3. Model choice
   • Pairwise vs listwise ranking, reward modeling, and/or RL from feedback.
   • How to combine safety and helpfulness objectives.
4. Offline training pipeline and feature/embedding generation
   • Data processing, feature sets, and embedding strategies.
   • Negative sampling and hard-negative mining.
5. Evaluation metrics
   • Ranking metrics (e.g., NDCG, MRR, pairwise accuracy).
   • Calibration and safety metrics.
6. Online inference architecture
   • Latency budgets, caching, and candidate generation.
   • Two-stage ranking (coarse-to-fine) and failover behavior.
7. Experimentation plan
   • A/B testing, interleaving, and counterfactual evaluation.
8. Safety and alignment measures
   • Toxicity filters, guardrails, and policy enforcement.
9. Bias and privacy controls
   • Fairness metrics, data minimization, and privacy-preserving training.
10. Monitoring and alerting
    • Quality, reliability, and drift detection.
11. Retraining cadence
    • Data refresh, active learning, and governance.
12. Cost and reliability trade-offs
    • Model size, serving hardware, and graceful degradation.

Provide a concise, high-level architecture description in words that ties the components together.