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Core
Model Debugging Methodology
Model debugging is the systematic work of root-causing why a model underperforms: judging whether the cause is the data, the features, the labels, model capacity, or the evaluation itself, rather than blindly tuning hyperparameters. The method leans on slice-level error analysis and the train/val/test gap ladder to pinpoint the failure before fixing it. AI, ML, and GenAI engineer interviews probe it because most candidates reach for bigger models or more tuning when the real bug is a leaky feature, a noisy label set, or a broken eval.
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