# Prompting for Discovery: Flexible Sense-Making for AI Art-Making with Dreamsheets

arXiv: https://arxiv.org/abs/2310.09985 · HTML: https://mlsanity.com/paper/2310.09985/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2023.

## Built on by (most cited first, 22 of 22)

- [Human-LLM Compound System for Scientific Ideation through Facet Recombination and Novelty Evaluation](https://mlsanity.com/paper/2409.14634/) (2024)
- [Patchview: LLM-Powered Worldbuilding with Generative Dust and Magnet Visualization](https://mlsanity.com/paper/2408.04112/) (2024)
- [AI-Instruments: Embodying Prompts as Instruments to Abstract & Reflect Graphical Interface Commands as General-Purpose Tools](https://mlsanity.com/paper/2502.18736/) (2025)
- [Exploring Empty Spaces: Human-in-the-Loop Data Augmentation](https://mlsanity.com/paper/2410.01088/) (2024)
- [DyME: Dynamic Multi-Concept Erasure in Diffusion Models with Bi-Level Orthogonal LoRA Adaptation](https://mlsanity.com/paper/2509.21433/) (2025)
- [DiscoverLLM: From Executing Intents to Discovering Them](https://mlsanity.com/paper/2602.03429/) (2026)
- [GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design](https://mlsanity.com/paper/2508.15227/) (2025)
- [AI-Assisted Causal Pathway Diagram for Human-Centered Design](https://mlsanity.com/paper/2403.08111/) (2024)
- [What Sketch Explainability Really Means for Downstream Tasks](https://mlsanity.com/paper/2403.09480/) (2024)
- [Large Language Models Meet User Interfaces: The Case of Provisioning Feedback](https://mlsanity.com/paper/2404.11072/) (2024)
- [Distribution-Conditional Generation: From Class Distribution to Creative Generation](https://mlsanity.com/paper/2505.03667/) (2025)
- [Reusing Computation in Text-to-Image Diffusion for Efficient Generation of Image Sets](https://mlsanity.com/paper/2508.21032/) (2025)
- [RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines](https://mlsanity.com/paper/2504.13587/) (2025)
- [StoryEnsemble: Enabling Dynamic Exploration & Iteration in the Design Process with AI and Forward-Backward Propagation](https://mlsanity.com/paper/2508.03182/) (2025)
- [Using customized GPT to develop prompting proficiency in architectural AI-generated images](https://mlsanity.com/paper/2504.13948/) (2025)
- [AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration](https://mlsanity.com/paper/2511.17906/) (2025)
- [ORIBA: Exploring LLM-Driven Role-Play Chatbot as a Creativity Support Tool for Original Character Artists](https://mlsanity.com/paper/2512.12630/) (2025)
- [A Large Language Model-based Computational Approach to Improve Identity-Related Write-Ups](https://mlsanity.com/paper/2312.16659/) (2023)
- [Agency Among Agents: Designing with Hypertextual Friction in the Algorithmic Web](https://mlsanity.com/paper/2507.23585/) (2025)
- [Stable diffusion models reveal a persisting human and AI gap in visual creativity](https://mlsanity.com/paper/2511.16814/) (2025)
- [Brief2Design: A Multi-phased, Compositional Approach to Prompt-based Graphic Design](https://mlsanity.com/paper/2604.11019/) (2026)
- [Compos3D: Interactive Part-Based Composition for Creative Control in Generative 3D Models](https://mlsanity.com/paper/2607.12193/) (2026)

## Abstract (arXiv)

Design space exploration (DSE) for Text-to-Image (TTI) models entails navigating a vast, opaque space of possible image outputs, through a commensurately vast input space of hyperparameters and prompt text. Minor adjustments to prompt input can surface unexpectedly disparate images. How can interfaces support end-users in reliably steering prompt-space explorations towards interesting results? Our design probe, DreamSheets, supports exploration strategies with LLM-based functions for assisted prompt construction and simultaneous display of generated results, hosted in a spreadsheet interface. The flexible layout and novel generative functions enable experimentation with user-defined workflows. Two studies, a preliminary lab study and a longitudinal study with five expert artists, revealed a set of strategies participants use to tackle the challenges of TTI design space exploration, and the interface features required to support them - like using text-generation to define local "axes" of exploration. We distill these insights into a UI mockup to guide future interfaces.