Model degradation occurs as the " Freshness " of the training data wanes and real-world conditions evolve.
The most robust control is " Periodic human reviews " (Human-in-the-Loop). Human experts can identify " drift " in logic or common-sense failures that automated systems might miss. Relying on model-generated data (Option A) can lead to " Model Collapse, " where the AI begins to drift into nonsensical patterns by reinforcing its own previous outputs. Human oversight ensures the model remains grounded in reality and aligned with business objectives.