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My fine-tuned model nails every question I test it with, and that success is what worries me instead of reassuring me.
What you'll have at the end
A held-out validation score, checked against the loss curve, that says memorized or generalized
You need
A fine-tuned model already trained, the exact set of examples it trained on, and a handful of labeled examples in the same format that were never fed into training.
Not covered
Retraining with a different base model or a larger dataset from scratch, and diagnosing skills the fine-tune lost outside this one task, a related but different failure.
Pick starting values for LoRA rank, alpha, and learning rate
for the rank, alpha, and learning rate settings least likely to push a retrain toward overfitting
Catastrophic forgetting wipes out skills your fine-tune never touched
when the failure is lost skills outside the fine-tuned task, not memorization of the training data itself
Checked 15 Aug 2026
Part of the LLM Fine-tuning cookbook