Prompt Chaining in Practice: A Case Study in Automated Scholarly Report Generation

提示词链实践：自动化科研报告生成的案例研究

Abstract: The exponential growth of scholarly publications requires automated tools for effective information synthesis. However, simple, single-shot prompting methods often lack the reliability and quality required for complex synthesis tasks. This paper introduces and empirically evaluates a multi-stage prompt chaining methodology as a more reliable architectural pattern for such tasks.

摘要： 学术出版物的指数级增长需要自动化工具来进行有效的信息综合。然而，简单的单次提示（single-shot prompting）方法往往缺乏复杂综合任务所需的可靠性和质量。本文介绍并实证评估了一种多阶段提示词链（prompt chaining）方法，将其作为此类任务中更可靠的架构模式。

This approach is implemented in our system, AI SciBrief, which automatically generates scholarly digests. We conducted a comparative experiment, measuring the performance of our prompt chaining method against a carefully optimized single-shot baseline. Both systems were evaluated against a human-authored “gold standard” report for the “Education” domain.

该方法已在我们的系统“AI SciBrief”中实现，用于自动生成学术摘要。我们进行了一项对比实验，衡量了我们的提示词链方法与经过精心优化的单次提示基准之间的性能差异。两个系统均针对“教育”领域的“黄金标准”人工撰写报告进行了评估。

The results demonstrate a significant difference in reliability: our prompt chaining method achieved a 100% success rate, whereas the optimized baseline failed in 50% of its runs. In terms of quality, the proposed method also demonstrated a clear advantage, achieving a superior ROUGE-L F1-score (0.507 vs. 0.486), driven primarily by higher precision.

结果显示，两者在可靠性方面存在显著差异：我们的提示词链方法实现了 100% 的成功率，而优化后的基准方法在 50% 的运行中失败。在质量方面，该方法也表现出明显的优势，获得了更高的 ROUGE-L F1 分数（0.507 对比 0.486），这主要得益于更高的精确度。

We conclude that prompt chaining is a more dependable and effective engineering approach for complex, multi-step generative tasks, significantly mitigating the risks of failure and inconsistency inherent in monolithic prompts.

我们得出结论：对于复杂的、多步骤的生成任务，提示词链是一种更可靠且有效的工程方法，它显著降低了单一提示词（monolithic prompts）固有的失败和不一致风险。

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Paper Details:

• Authors: Andrei Lazarev
• arXiv ID: 2607.27210
• Subject: Computation and Language (cs.CL)
• Published: 6 May 2026

论文详情：

• 作者： Andrei Lazarev
• arXiv ID： 2607.27210
• 学科： 计算与语言 (cs.CL)
• 发布日期： 2026年5月6日