Your fine-tuned model reproduces training examples word for word instead of generalizing. How do you fix it?
A paraphrased prompt comes back with a training answer, verbatim. This is small-dataset SFT overfitting, not the pretraining memorization problem people confuse it with, and the fix has a strict order of operations.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A paraphrased prompt comes back with a training answer, verbatim. This is small-dataset SFT overfitting, not the pretraining memorization problem people confuse it with, and the fix has a strict order of operations.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
