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July 8, 2026Big Data and Cognitive ComputingOpen Access

Better Prompts, Better Usefulness: A Systematic Review and Experimental Evaluation of Structured Prompting Techniques in Large Language Models

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Authors

ACAlessia CantiniAMAndrea De Mauro

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Overview

Systematic review and experimental evaluation show that structured prompting boosts usefulness in business tasks, suggesting robust benefits for human-AI interaction.

Key Points

  • This research aims to evaluate how structured prompting techniques influence the usefulness of outputs generated by large language models in business contexts.
  • Conducted a systematic literature review following PRISMA guidelines to identify prompt enhancement strategies.
  • Developed a taxonomy distinguishing between task-alignment and reasoning-transparency techniques.
  • Designed a controlled experimental study where knowledge workers evaluated LLM outputs for analytical and summarization tasks.
  • Structured prompting significantly increases perceived usefulness compared to baseline approaches.
  • The combination of example-based conditioning and explicit reasoning scaffolding yields the highest evaluations.
  • The moderating effect of AI usage frequency is not statistically significant, indicating robustness across experience levels.

Cite This Study

Cantini et al. (2026) studied this question.

synapsesocial.com/papers/6a4dea28d2ea289ef628404ahttps://doi.org/10.3390/bdcc10070224
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