The size of vision models has grown exponentially in recent years, particularly with the rise of Vision Transformers. This rapid growth has driven the development of parameter-efficient tuning methods, such as learning adapter layers or low-rank adaptation layers, which enable fine-tuning of a small subset of model parameters while keeping the vast majority of pretrained parameters frozen. However, designing an effective tuning method is not straightforward: it often involves exploring numerous design choices, and each downstream dataset may require custom-tailored solutions. In this chapter, we introduce Neural prOmpt seArcH (NOAH), a novel approach that leverages a neural architecture search algorithm to automatically learn the optimal design of prompt modules for large vision models, tailored specifically for each downstream dataset.