Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows

TL;DR

This paper introduces IntentTagger, a system using Intent Tags to facilitate granular, flexible human-AI co-creation workflows, addressing challenges in aligning AI outputs with user intentions and improving prompt formulation.

Contribution

It presents a novel Intent Tag-based approach and explores micro-prompting interactions to enhance user control and flexibility in GenAI content creation workflows.

Findings

Intent tags improve expression of user intent across ambiguity levels

Micro-prompting enables non-linear, flexible interactions with GenAI

User study highlights benefits and challenges of intent tag workflows

Abstract

Despite Generative AI (GenAI) systems' potential for enhancing content creation, users often struggle to effectively integrate GenAI into their creative workflows. Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to the AI system (prompt formulation), and insufficient flexibility of AI systems to support diverse creative workflows (workflow flexibility). Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags - small, atomic conceptual units that encapsulate user intent - for exploring granular and non-linear micro-prompting interactions for Human-GenAI co-creation workflows. Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven workflows. We conclude by discussing the broader implications of our findings and design considerations for GenAI-supported content creation workflows.