Individual skill
regex-vs-llm-structured-text
Decision framework for parsing structured text (quizzes, forms, invoices, receipts, tables) with a hybrid regex-first pipeline — regex extraction handles 95%+ cheaply, a confidence scorer flags low-confidence items, and an LLM validator fixes only the edge cases. Use when choosing between regex and LLM for text extraction, building a cheap document parser, or optimizing extraction cost and accuracy.
pi/core/skills/regex-vs-llm-structured-text/SKILL.md
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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
- engineering Derived category
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# Regex vs LLM for Structured Text Parsing
A practical decision framework for parsing structured text (quizzes, forms, invoices, documents). The key insight: regex handles 95-98% of cases cheaply and deterministically. Reserve expensive LLM calls for the remaining edge cases.
## When to Activate
- Parsing structured text with repeating patterns (questions, forms, tables)
- Deciding between regex and LLM for text extraction
- Building hybrid pipelines that combine both approaches
- Optimizing cost/accuracy tradeoffs in text processing
## Decision Framework
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