sft

Instruction tuning converts a bare next-token predictor into a model that obeys instructions. What you are being tested on is slotting it correctly into the pretrain to SFT to alignment pipeline and stating precisely what it does and does not repair.

A few thousand carefully chosen examples can beat a million scraped ones. The signal is knowing which filters matter, how you gauge example quality, and why diversity beats raw volume.

Distillation is not a single method. The signal is knowing when to match logits versus train on generated text, why on-policy distillation beats off-policy, and how reasoning models are distilled.

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.

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.

Every LLM is one function: tokens in, a distribution over the next token out. The signal is what you say next, because the distance from that function to a useful assistant is three training stages, and almost every production complaint lands in exactly one of them.

The follow-up every interviewer holds in reserve after you say you fine-tuned something. A task-metric win proves almost nothing on its own. Here is the five-part protocol that separates a shipped model from a demo.

The staged recipe is the easy half. The half that sinks real projects is data licensing, an evaluation bar that needs actual domain experts, and knowing which parts of the problem fine-tuning cannot solve at all.