Your question is Continuous Feedback Fine-Tuning. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Architecture a feedback loop that continuously fine-tunes a model using user interaction data while preventing catastrophic forgetting.
Explain how interaction events become training data, how you would distinguish useful feedback from biased or corrupted signals, and how models would be evaluated and promoted. Address online versus batch updates, replay or regularization strategies, rollback, training-serving skew, feature drift, privacy, monitoring, and resource constraints. Make reasonable assumptions where scale or latency details are unspecified, and state the tradeoffs behind your choices.
