Intent tagging is an interaction technique in generative artificial intelligence workflows where a user expresses and organizes creative intent through small, modular conceptual units rather than traditional monolithic text prompts. These discrete tags encapsulate atomic aspects of a goal, such as style, structure, tone, or specific functional requirements, which users can assemble, modify, and attach to different parts of a project. By decomposing complex instructions into manageable micro-prompts, intent tagging enables granular and non-linear control over generation, mitigates the difficulty of prompt formulation, and improves alignment between user expectations and system outputs during iterative co-creation.
