Wordflow: Social Prompt Engineering for Large Language Models
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Abstract (arXiv)
Large language models (LLMs) require well-crafted prompts for effective use. Prompt engineering, the process of designing prompts, is challenging, particularly for non-experts who are less familiar with AI technologies. While researchers have proposed techniques and tools to assist LLM users in prompt design, these works primarily target AI application developers rather than non-experts. To address this research gap, we propose social prompt engineering, a novel paradigm that leverages social computing techniques to facilitate collaborative prompt design. To investigate social prompt engineering, we introduce Wordflow, an open-source and social text editor that enables everyday users to easily create, run, share, and discover LLM prompts. Additionally, by leveraging modern web technologies, Wordflow allows users to run LLMs locally and privately in their browsers. Two usage scenarios highlight how social prompt engineering and our tool can enhance laypeople's interaction with LLMs. Wordflow is publicly accessible at https://poloclub.github.io/wordflow.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 21 of its 67 references that are in the map. First posted on arXiv 25 Jan 2024.
Builds on
21 of 67 references.
References · 21
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
- 2023Large Language Modelscited by 780
- 2024TinyLlama: An Open-Source Small Language Modelcited by 618
- 2022DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Modelscited by 379
- 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 329
- 2022PromptSource: An Integrated Development Environment and Repository for Natural Language Promptscited by 289
- 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 268
- 2023Efficient Guided Generation for Large Language Modelscited by 200
- 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
9 more
- 2022PromptChainer: Chaining Large Language Model Prompts through Visual Programmingcited by 134
- 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
- 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
- 2022A Taxonomy of Prompt Modifiers for Text-To-Image Generationcited by 55
- 2023Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chainscited by 24
- 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
- 2023PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Modelscited by 19
- 2023CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programmingcited by 16
- 2023Revisiting Prompt Engineering via Declarative Crowdsourcingcited by 12
Built on by
Citing papers per year: 2024: 6; 2025: 3.
9 papers.
- 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
- 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Promptscited by 19
- 2024GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agencycited by 18
- 2024MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generationcited by 13
- 2024Can LLMs Understand Computer Networks? Towards a Virtual System Administratorcited by 11
- 2024PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creationcited by 9
- 2025ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attackscited by 9
- 2025Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Designcited by 2
- 2025Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocationscited by 1
- 2025Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations29 Sep 2025
- 2025ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks2 Jul 2025
- 2025Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Design22 Apr 2025
- 2024PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation21 Oct 2024
- 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts19 Sep 2024
- 2024MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation2 Jul 2024
- 2024Can LLMs Understand Computer Networks? Towards a Virtual System Administrator19 Apr 2024
- 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototyping23 Feb 2024
- 2024GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency13 Feb 2024