别制造一个你无法衡量的提示词裁剪
本文讨论了在不影响模型性能的前提下缩短提示词（Prompt）的方法。核心观点是：缩短提示词时必须设置可量化的评估指标，否则可能因过度裁剪而导致输出质量下降。作者提出了一套系统性方法，包括基准测试、逐步裁剪和回归测试，帮助开发者在优化成本与延迟的同时确保模型行为不发生退化。
背景速读
- 这篇文章讨论的是大语言模型（LLM）的**提示词（prompt）工程**中的一个具体问题：把提示写得太短，反而让模型表现变差。
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