Wordflow: Social Prompt Engineering for Large Language Models
This addresses the problem of making LLM prompt engineering more accessible for non-experts, though it is incremental as it builds on existing social computing techniques.
The paper tackles the challenge of prompt engineering for large language models (LLMs) by introducing social prompt engineering, a novel paradigm that uses social computing to enable collaborative prompt design, resulting in Wordflow, an open-source tool that allows everyday users to create, run, share, and discover prompts locally and privately in their browsers.
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.