A method in machine learning where a model, typically a large pre-trained language model, is fine-tuned by adjusting the prompts or instructions given to it, rather than altering the model’s internal parameters. This approach allows for more efficient customization of the model’s responses to specific tasks or datasets with minimal additional training.