Prompt Smells: An Omen for Undesirable Generative AI Outputs
Not on the map. Why?
Abstract (arXiv)
Recent Generative Artificial Intelligence (GenAI) trends focus on various applications, including creating stories, illustrations, poems, articles, computer code, music compositions, and videos. Extrinsic hallucinations are a critical limitation of such GenAI, which can lead to significant challenges in achieving and maintaining the trustworthiness of GenAI. In this paper, we propose two new concepts that we believe will aid the research community in addressing limitations associated with the application of GenAI models. First, we propose a definition for the "desirability" of GenAI outputs and three factors which are observed to influence it. Second, drawing inspiration from Martin Fowler's code smells, we propose the concept of "prompt smells" and the adverse effects they are observed to have on the desirability of GenAI outputs. We expect our work will contribute to the ongoing conversation about the desirability of GenAI outputs and help advance the field in a meaningful way.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 23 Jan 2024.
Builds on
10 of 16 references.
References · 10
- 2022Generative Adversarial Networkscited by 5,603
- 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
- 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
- 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
- 2021True Few-Shot Learning with Language Modelscited by 343
- 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 216
- 2021An Introduction to Deep Generative Modelingcited by 104
- 2023Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chainscited by 24
- 2023ChatGPT as a tool for User Story Quality Evaluation: Trustworthy Out of the Box?cited by 13
- 2023RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systemscited by 4
Built on by
Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptionscited by 15
- 2025LLM-Based Social Simulations Require a Boundarycited by 11
- 2025Talking Tennis: Language Feedback from 3D Biomechanical Action Recognitioncited by 0
- 2025Specification and Detection of LLM Code Smellscited by 0
- 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions16 Feb 2026
- 2025Specification and Detection of LLM Code Smells19 Dec 2025
- 2025Talking Tennis: Language Feedback from 3D Biomechanical Action Recognition4 Oct 2025
- 2025LLM-Based Social Simulations Require a Boundary24 Jun 2025