Prompt Editor: A Taxonomy-driven System for Guided LLM Prompt Development in Enterprise Settings
Summary: Taxonomy-driven Prompt Editor learns segment types from an organization prompt corpus, enabling automatic segmentation and guided refinement for enterprise LLM prompts. Human-in-the-loop feedback and corpus-based init improve LLM prompts for data extraction. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jeffery Cao (Celonis Inc.)
- 2. Lampros Flokas (Celonis Inc.)
- 3. Yujian Xu (Celonis Inc.)
- 4. Eugene Wu (Celonis Inc.; Columbia University)
- 5. Xu Chu (Celonis Inc.)
- 6. Cong Yu (Celonis Inc.)
BibTeX Citation
@inproceedings{cao_sigmod25,
title = {{Prompt Editor: A Taxonomy-driven System for Guided LLM Prompt Development in Enterprise Settings}},
author = {Cao, Jeffery and Flokas, Lampros and Xu, Yujian and Wu, Eugene and Chu, Xu and Yu, Cong},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725124},
url = {https://dl.acm.org/doi/10.1145/3722212.3725124},
year = {2025}
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| 4,621 | spade: Synthesizing Data Quality Assertions for Large Language Model Pipelines | 2024 | VLDB | 6.6024412e-05 |
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