Less Back-and-Forth: A Comparative Study of Structured Prompting

2026-05-19

AI Summary

This study addresses the challenge of low response quality and excessive user interaction in open-ended tasks due to ambiguous prompts in large language models. The authors propose a checklist-style prompting method and systematically evaluate its impact on response quality and user effort across four task categories, comparing it against baseline and clarification-based prompting strategies. Using a unified rubric assessing task completion, correctness, adherence to instructions, and clarity, experiments conducted on ChatGPT, Claude, and Grok demonstrate that checklist prompting achieves an average score of 7.50 out of 8—significantly outperforming both baseline prompting (5.67) and clarification-based prompting (6.67). Moreover, this approach reduces both the number of interaction turns and input token consumption, thereby achieving a superior trade-off between output quality and interaction efficiency.

Abstract

Large language models (LLMs) are widely used for open-ended tasks, but underspecified prompts can lead to low-quality answers and additional interaction. This paper studies whether structured prompt design improves response quality while reducing user effort. We compare three prompt conditions: a raw prompt, a checklist-improved prompt, and a clarifying-question prompt. We evaluate these conditions across four task types--summarization, planning, explanation, and coding--using three LLM systems: ChatGPT, Claude, and Grok. Each output is scored with a unified rubric covering task completion, correctness, compliance, and clarity. Checklist-improved prompts achieved the highest mean rubric score, 7.50 out of 8, compared with 5.67 for raw prompts and 6.67 for clarifying-question prompts. Checklist prompts also produced the best quality-effort tradeoff, using fewer average tokens than both raw and clarifying prompts. These results suggest that a simple prompt checklist can improve LLM responses while reducing unnecessary interaction.

Problem

• large language models
• prompt design
• response quality
• user effort
• structured prompting

Innovation

• structured prompting
• prompt checklist
• large language models
• user effort reduction
• response quality