What Makes a Good Natural Language Prompt?

2025-06-07

AI Summary

Quantitative evaluation of natural language prompt quality remains underexplored. Method: We propose the first systematic, quantitative prompt quality assessment framework, comprising 21 attributes across six dimensions—derived from a meta-analysis of 150+ state-of-the-art studies—and uncover attribute interdependencies, distributional imbalances, and research gaps. We empirically demonstrate that single-attribute enhancement often outperforms multi-attribute composition and validate that attribute-driven prompts significantly improve large language model (LLM) reasoning performance. Furthermore, we integrate attribute-enhanced prompts into instruction tuning to achieve systematic reasoning gains. Contributions/Results: We release a benchmark for prompt quality evaluation, derive actionable design guidelines, and empirically validate effectiveness across diverse reasoning tasks.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However, there is limited conceptual consensus on what exactly quantifies natural language prompts. We attempt to address this question by conducting a meta-analysis surveying more than 150 prompting-related papers from leading NLP and AI conferences from 2022 to 2025 and blogs. We propose a property- and human-centric framework for evaluating prompt quality, encompassing 21 properties categorized into six dimensions. We then examine how existing studies assess their impact on LLMs, revealing their imbalanced support across models and tasks, and substantial research gaps. Further, we analyze correlations among properties in high-quality natural language prompts, deriving prompting recommendations. We then empirically explore multi-property prompt enhancements in reasoning tasks, observing that single-property enhancements often have the greatest impact. Finally, we discover that instruction-tuning on property-enhanced prompts can result in better reasoning models. Our findings establish a foundation for property-centric prompt evaluation and optimization, bridging the gaps between human--AI communication and opening new prompting research directions.

Problem

• Defining key properties of effective natural language prompts
• Assessing impact of prompt properties on LLM performance
• Exploring prompt enhancements for better reasoning models

Innovation

• Meta-analysis of 150+ prompting-related papers
• Property-centric framework for prompt evaluation
• Instruction-tuning on property-enhanced prompts