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Wordflow: Social Prompt Engineering for Large Language Models

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arxiv 2401.14447 v1 pith:Z6GSFGLN submitted 2024-01-25 cs.HC cs.AIcs.CLcs.LG

classification cs.HCcs.AIcs.CLcs.LG
keywords promptsocialengineeringwordflowllmspromptsusersdesign
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Design

    cs.CY 2025-04 conditional novelty 4.0 of 10

    The paper organizes responsible prompt engineering into five components and argues that prompt-level practices let deployers steer AI outputs toward ethical outcomes without model retraining.

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