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Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality
- Source
- arxiv.org
- Author
- Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
- Date
Why it matters
Prompt robustness is not luck: naming domain terms precisely and giving explicit action directives narrows the model's interpretive space, and the prompts that score highest also vary least across rewordings.
Terms in this piece · Glossary
- token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Read the source arxiv.org
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