Explain LoRA, QLoRA, and parameter-efficient fine-tuning. Why train a fraction of the parameters?
PEFT is how everyone fine-tunes large models now. The signal is the low-rank insight behind LoRA, why it cuts memory so much, and what 4-bit QLoRA adds. Here is the answer that goes past 'it's efficient fine-tuning.'
Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.
PEFT is how everyone fine-tunes large models now. The signal is the low-rank insight behind LoRA, why it cuts memory so much, and what 4-bit QLoRA adds. Here is the answer that goes past 'it's efficient fine-tuning.'
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.