Prompt Chaining in Practice: A Case Study in Automated Scholarly Report Generation
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 sta...
| Authors | Andrei Lazarev |
|---|---|
| Primary category | cs.CL |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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