Individual skill
regex-vs-llm-structured-text
在解析结构化文本时，用于在正则表达式（Regex）和大型语言模型（LLM）之间进行选择的决策框架——优先使用正则表达式，仅针对低置信度的边界情况引入 LLM。
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- ai-agents Repository topic
- anthropic Repository topic
- claude Repository topic
- claude-code Repository topic
- developer-tools Repository topic
- llm Repository topic
- mcp Repository topic
- productivity Repository topic
- everything-claude-code Repository topic
- oneskill Repository topic
- engineering Derived category
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# 结构化文本解析：正则表达式（Regex）与大语言模型（LLM）的抉择
这是一个用于解析结构化文本（测验、表单、发票、文档）的实用决策框架。核心见解是：正则表达式（Regex）能以极低的成本确定性地处理 95-98% 的情况。应将昂贵的 LLM 调用保留给剩余的边界情况（Edge Cases）。
## 何时激活（When to Activate）
- 解析具有重复模式的结构化文本（问题、表单、表格等）
- 在文本提取任务中权衡使用正则表达式还是 LLM
- 构建结合这两种方法的混合流水线（Hybrid Pipelines）
- 在文本处理中优化成本与准确性的平衡
## 决策框架（Decision Framework）
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