A retail company wants to customize a large foundation model to consistently follow their internal product-naming conventions and writing style. Their ML team has a modest compute budget and wants to avoid the cost and time of retraining all of the model's parameters. They also want to keep the original base model intact so it can still serve other general tasks. Which customization approach best fits these constraints?