Wordflow: Social Prompt Engineering for Large Language Models
Zijie J. Wang Aishwarya Chakravarthy David Munechika Duen Horng Chau
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.