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

arXiv: https://arxiv.org/abs/2502.18737 · HTML: https://mlsanity.com/paper/2502.18737/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 107 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 30 of 30)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://mlsanity.com/paper/2010.15980/) (2020)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces](https://mlsanity.com/paper/2309.14459/) (2023)
- [DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents](https://mlsanity.com/paper/2101.11796/) (2021)
- [Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models](https://mlsanity.com/paper/2304.09337/) (2023)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation](https://mlsanity.com/paper/2310.12953/) (2023)
- ["What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models](https://mlsanity.com/paper/2304.06597/) (2023)
- [Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models](https://mlsanity.com/paper/2305.11483/) (2023)
- [Opal: Multimodal Image Generation for News Illustration](https://mlsanity.com/paper/2204.09007/) (2022)
- [PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions](https://mlsanity.com/paper/2308.05184/) (2023)
- [DirectGPT: A Direct Manipulation Interface to Interact with Large Language Models](https://mlsanity.com/paper/2310.03691/) (2023)
- [Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems](https://mlsanity.com/paper/2305.07465/) (2023)
- [User Intent Prediction in Information-seeking Conversations](https://mlsanity.com/paper/1901.03489/) (2019)
- [GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency](https://mlsanity.com/paper/2402.08855/) (2024)
- [Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools](https://mlsanity.com/paper/2303.00192/) (2023)
- [Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrots](https://mlsanity.com/paper/2307.10751/) (2023)
- [Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models](https://mlsanity.com/paper/2310.02161/) (2023)
- [Automatically Generating Engaging Presentation Slide Decks](https://mlsanity.com/paper/1903.09308/) (2019)
- [Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation](https://mlsanity.com/paper/2402.07342/) (2024)
- [Foundation models in brief: A historical, socio-technical focus](https://mlsanity.com/paper/2212.08967/) (2022)
- [OutlineSpark: Igniting AI-powered Presentation Slides Creation from Computational Notebooks through Outlines](https://mlsanity.com/paper/2403.09121/) (2024)
- [Languages of Play: Towards semantic foundations for game interfaces](https://mlsanity.com/paper/1703.05410/) (2017)

## Built on by (most cited first, 6 of 6)

- [From Words to Widgets for Controllable LLM Generation](https://mlsanity.com/paper/2604.10925/) (2026)
- [Alignment-Process-Outcome: Rethinking How AIs and Humans Collaborate](https://mlsanity.com/paper/2603.08017/) (2026)
- [CogInstrument: Modeling Cognitive Processes for Bidirectional Human-LLM Alignment in Planning Tasks](https://mlsanity.com/paper/2604.10587/) (2026)
- [Brief2Design: A Multi-phased, Compositional Approach to Prompt-based Graphic Design](https://mlsanity.com/paper/2604.11019/) (2026)
- [PeerPen: AI-Assisted Writing for Online Mental Health Peer Support](https://mlsanity.com/paper/2609.14886/) (2026)
- [Alignment Games: A Framework for Conceptual Repair in Human-AI Collaboration](https://mlsanity.com/paper/2609.35197/) (2026)

## Abstract (arXiv)

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.