A Systematic Framework for Visual Prompting in Multimodal AI Systems

Visual prompting has emerged as a foundational interaction paradigm in multimodal artificial intelligence, yet existing literature treats prompting techniques as fragmented task implementations rather than a coherent design space. This paper proposes a systematic framework for understanding visual prompting methods through four analytical dimensions: input structure, user intent, visual grounding, and output type. We present a taxonomy of nine distinct prompting methods-from question-centric (VQA) to generative-transformative approaches-derived from analysis of current multimodal system capabilities. Our cross-method analysis reveals critical trade-offs between grounding precision and reasoning reliability, as well as between prompt generality and user controllability. We contribute design guidelines for practitioners selecting prompting strategies and identify research opportunities in compositional prompting, robustness evaluation, and multimodal prompt languages. By reframing visual prompting as an interaction design problem rather than merely an input format, this work establishes foundations for principled development of human-AI collaborative systems.