regex-vs-llm-structured-text
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
Is the text format consistent and repeating?
├── Yes (>90% follows a pattern) → Start with Regex
│ ├── Regex handles 95%+ → Done, no LLM needed
│ └── Regex handles <95% → Add LLM for edge cases only
└── No (free-form, highly variable) → Use LLM directly
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