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