fine tune
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Retrain when the data distribution changes significantly; fine-tune when behavior needs adjustment.
If core patterns shift, fine-tuning may not be enough. If the task remains similar but requirements evolve, fine-tuning is more efficient.
Evaluate both paths on a validation set before committing.
Common mistakes:
Fine-tuning outdated models
Retraining unnecessarily
Ignoring data diagnostics
Choose the strategy that matches the change.