Compare LoRA, prefix tuning, prompt tuning, and full fine-tuning, and state when each is the right choice.

LoRA freezes the base weight and learns a low-rank update BA, reducing trainable parameters and optimizer state. Rank controls adaptation capacity; choose it by task complexity, target modules, validation quality, and memory budget.

Use equations or tensor shapes where they clarify the claim, then name an experiment or ablation that would distinguish competing explanations.