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

Researchers introduced AI SciBrief, a system using multi-stage prompt chaining to generate scholarly digests, achieving a 100% success rate versus a 50% failure rate for a single-shot baseline in a comparative experiment. The prompt chaining method also achieved a higher ROUGE-L F1-score (0.507 vs. 0.486) against a human-authored gold standard, demonstrating improved reliability and quality for complex synthesis tasks.

arXiv:2607.27210v1 Announce Type: new 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. 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. 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. 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.