# Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering

arXiv: https://arxiv.org/abs/2303.13534 · HTML: https://mlsanity.com/paper/2303.13534/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2023.

## Built on by (most cited first, 30 of 43)

- [Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study](https://mlsanity.com/paper/2305.13860/) (2023)
- [MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots](https://mlsanity.com/paper/2307.08715/) (2023)
- [In-Context Impersonation Reveals Large Language Models' Strengths and Biases](https://mlsanity.com/paper/2305.14930/) (2023)
- [DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer](https://mlsanity.com/paper/2312.03724/) (2023)
- [FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2309.05274/) (2023)
- [Using Text-to-Image Generation for Architectural Design Ideation](https://mlsanity.com/paper/2304.10182/) (2023)
- [Can Developers Prompt? A Controlled Experiment for Code Documentation Generation](https://mlsanity.com/paper/2408.00686/) (2024)
- [Pencils to Pixels: A Systematic Study of Creative Drawings across Children, Adults and AI](https://mlsanity.com/paper/2502.05999/) (2025)
- [SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches](https://mlsanity.com/paper/2502.07556/) (2025)
- [POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation](https://mlsanity.com/paper/2504.13392/) (2025)
- [Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems](https://mlsanity.com/paper/2505.15741/) (2025)
- [Perceptions and Realities of Text-to-Image Generation](https://mlsanity.com/paper/2306.08363/) (2023)
- [Exploring Prompt Engineering: A Systematic Review with SWOT Analysis](https://mlsanity.com/paper/2410.12843/) (2024)
- [GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design](https://mlsanity.com/paper/2508.15227/) (2025)
- [Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists](https://mlsanity.com/paper/2501.12374/) (2025)
- [Distribution-Conditional Generation: From Class Distribution to Creative Generation](https://mlsanity.com/paper/2505.03667/) (2025)
- [A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation](https://mlsanity.com/paper/2511.19004/) (2025)
- [Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspective](https://mlsanity.com/paper/2306.01798/) (2023)
- [IteraTTA: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models](https://mlsanity.com/paper/2307.13005/) (2023)
- [Prompt Engineer: Analyzing Hard and Soft Skill Requirements in the AI Job Market](https://mlsanity.com/paper/2506.00058/) (2025)
- [Data-Driven Analysis of Text-Conditioned AI-Generated Music: A Case Study with Suno and Udio](https://mlsanity.com/paper/2509.11824/) (2025)
- [Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPT](https://mlsanity.com/paper/2307.05493/) (2023)
- [Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification](https://mlsanity.com/paper/2405.15115/) (2024)
- [Inference time LLM alignment in single and multidomain preference spectrum](https://mlsanity.com/paper/2410.19206/) (2024)
- [A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Tasks](https://mlsanity.com/paper/2504.20340/) (2025)
- [ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation](https://mlsanity.com/paper/2508.06065/) (2025)
- [Towards Effective Prompt Stealing Attack against Text-to-Image Diffusion Models](https://mlsanity.com/paper/2508.06837/) (2025)
- [Enhancing Patient-Centric Communication: Leveraging LLMs to Simulate Patient Perspectives](https://mlsanity.com/paper/2501.06964/) (2025)
- [DangerMaps: Personalized Safety Advice for Travel in Urban Environments using a Retrieval-Augmented Language Model](https://mlsanity.com/paper/2503.14103/) (2025)
- [Steerable Chatbots: Exploring Personalization Control Interfaces via LLM Activation Steering](https://mlsanity.com/paper/2505.04260/) (2025)

## Abstract (arXiv)

We are witnessing a novel era of creativity where anyone can create digital content via prompt-based learning (known as prompt engineering). This paper investigates prompt engineering as a novel creative skill for creating AI art with text-to-image generation. In three consecutive studies, we explore whether crowdsourced participants can 1) discern prompt quality, 2) write prompts, and 3) refine prompts. We find that participants could evaluate prompt quality and crafted descriptive prompts, but they lacked style-specific vocabulary necessary for effective prompting. This is in line with our hypothesis that prompt engineering is a new type of skill that is non-intuitive and must first be acquired (e.g., through means of practice and learning) before it can be used. Our studies deepen our understanding of prompt engineering and chart future research directions. We conclude by envisioning four potential futures for prompt engineering.