What You Say = What You Want? Teaching Humans to Articulate Requirements for LLMs

• URL: http://arxiv.org/abs/2409.08775v1
• Date: Fri, 13 Sep 2024 12:34:14 GMT
• Title: What You Say = What You Want? Teaching Humans to Articulate Requirements for LLMs
• Authors: Qianou Ma, Weirui Peng, Hua Shen, Kenneth Koedinger, Tongshuang Wu,
• Abstract summary: We introduce Requirement-Oriented Prompt Engineering (ROPE), a paradigm that focuses human attention on generating clear, complete requirements during prompting. In a study with 30 novices, we show that requirement-focused training doubles novices' prompting performance, significantly outperforming conventional prompt engineering training and prompt optimization. Our work paves the way for more effective task delegation in human-LLM collaborative prompting.
• Score: 26.398086645901742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompting ChatGPT to achieve complex goals (e.g., creating a customer support chatbot) often demands meticulous prompt engineering, including aspects like fluent writing and chain-of-thought techniques. While emerging prompt optimizers can automatically refine many of these aspects, we argue that clearly conveying customized requirements (e.g., how to handle diverse inputs) remains a human-centric challenge. In this work, we introduce Requirement-Oriented Prompt Engineering (ROPE), a paradigm that focuses human attention on generating clear, complete requirements during prompting. We implement ROPE through an assessment and training suite that provides deliberate practice with LLM-generated feedback. In a study with 30 novices, we show that requirement-focused training doubles novices' prompting performance, significantly outperforming conventional prompt engineering training and prompt optimization. We also demonstrate that high-quality LLM outputs are directly tied to the quality of input requirements. Our work paves the way for more effective task delegation in human-LLM collaborative prompting.

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