What are the key hyperparameters for fine-tuning an LLM, and how do you actually set them?
Everyone can name learning rate and epochs. The signal is knowing that LoRA wants a learning rate roughly 10x higher than full fine-tuning, why alpha/r is the only scaling that matters, and which curve tells you your rank is too big.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Everyone can name learning rate and epochs. The signal is knowing that LoRA wants a learning rate roughly 10x higher than full fine-tuning, why alpha/r is the only scaling that matters, and which curve tells you your rank is too big.
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.
