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

TL;DR

This study demonstrates that structured prompts, especially checklist-based ones, enhance large language model response quality and efficiency across various tasks, reducing user effort and interaction.

Contribution

It introduces and empirically evaluates structured prompt designs, showing their effectiveness in improving LLM output quality and reducing interaction compared to unstructured prompts.

Findings

Checklist prompts scored highest on quality rubrics.

Checklist prompts used fewer tokens, indicating efficiency.

Structured prompts improve response quality across multiple tasks.

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.