Prompt tuning adds learnable embeddings to the input sequence. These embeddings are trained while the model stays frozen.
Think of it as learning a soft prompt that optimally guides the model. The learned embeddings can encode task-specific context that would be hard to express in text.
Parameter efficient but less flexible than LoRA. Best for classification and simple generation tasks.