What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use

• URL: http://arxiv.org/abs/2409.08775v3
• Date: Mon, 28 Apr 2025 16:07:05 GMT
• Title: What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use
• Authors: Qianou Ma, Weirui Peng, Chenyang Yang, Hua Shen, Kenneth Koedinger, Tongshuang Wu,
• Abstract summary: Existing prompt engineering instructions often lack focused training on requirement articulation.<n>We introduce Requirement-Oriented Prompt Engineering (ROPE), a paradigm that focuses human attention on generating clear, complete requirements.<n>In a randomized controlled experiment with 30 novices, ROPE significantly outperforms conventional prompt engineering training.
• Score: 30.933375576806156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompting LLMs for complex tasks (e.g., building a trip advisor chatbot) needs humans to clearly articulate customized requirements (e.g., "start the response with a tl;dr"). However, existing prompt engineering instructions often lack focused training on requirement articulation and instead tend to emphasize increasingly automatable strategies (e.g., tricks like adding role-plays and "think step-by-step"). To address the gap, 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 randomized controlled experiment with 30 novices, ROPE significantly outperforms conventional prompt engineering training (20% vs. 1% gains), a gap that automatic prompt optimization cannot close. Furthermore, we demonstrate a direct correlation between the quality of input requirements and LLM outputs. Our work paves the way to empower more end-users to build complex LLM applications.

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