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药学科普中提示词工程的应用与展望
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| 篇名： | 药学科普中提示词工程的应用与展望 |
| TITLE： | Applications and prospects of prompt engineering in pharmaceutical popularization |
| 摘要： | 目的 为构建药学科普中大语言模型的提示词工程体系，为药师群体开展高效、规范的科普工作提供参考。方法系统阐述提示词工程的原理、类别及其对模型的输出“幻觉”、可解释性不足等问题的缓解作用。明确两大核心场景下（“文生文”和“文生图”）提示词工程的设计及优化，结合实例对比应用前后的输出效果，并指出现阶段提示词工程在药学科普领域应用的局限性。结果在两大药学科普的应用场景下，合理的提示词工程能提高大语言模型输出的准确性、可读性及效率，生成适配临床应用场景的个性化科普内容。结论提示词工程可优化药学科普的输出质量。本文构建的针对药学科普的规范化提示词工程模板，可提升药师开展科普创作的效率和质量。 |
| ABSTRACT： | OBJECTIVE This study aims to establish a prompt engineering system for large language models in pharmaceutical popularization， and provide references for pharmacists to carry out efficient and standardized science popularization work. METHODS This study systematically expounded the principles and classifications of prompt engineering， as well as its effect on alleviating problems including model output hallucinations and poor interpretability. The design and optimization strategies of prompt engineering were defined for two core scenarios， namely text-to-text and text-to-image. Typical examples were adopted to compare the output effects before and after application. In addition， the limitations of prompt engineering applied in pharmaceutical popularization at the current stage were summarized. RESULTS In the two major scenarios of pharmaceutical popularization， well-designed prompt engineering improved the accuracy， readability and efficiency of outputs generated by large language models， and produced personalized popularization content adapted to clinical practice. CONCLUSIONS Prompt engineering can effectively improve the output quality of pharmaceutical popularization. The formulated standardized prompt engineering templates tailored for pharmaceutical popularization， can help pharmacists improve the efficiency and quality of popularization content creation. |
| 期刊： | 2026年第37卷第11期 |
| 作者： | 樊鑫怡；钱妍；朱鸣阳 |
| AUTHORS： | FAN Xinyi，QIAN Yan，ZHU Mingyang |
| 关键字： | 人工智能; 大语言模型; 药学科普; 提示词工程; 人机协同 |
| KEYWORDS： | artificial intelligence; large language models; pharmaceutical popularization; prompt engineering; human-machine collaboration |
| 阅读数： | 5 次 |
| 本月下载数： | 0 次 |
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