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.
- Kind
- Skill
- Works with
- Claude Code, Codex
- Source
- affaan-m/ECC @ d3b8a3e90890
- Path
- skills/regex-vs-llm-structured-text/SKILL.md
- License
- MIT
- Jobs
- Debugging, Documentation, Automation
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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
```
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
```
## Architecture Pattern
```
Source Text
│
▼
[Regex Parser] ─── Extracts structure (95-98% accuracy)
│
▼
[Text Cleaner] ─── Removes noise (markers, page numbers, artifacts)
│
▼
[Confidence Scorer] ─── Flags low-confidence extractions
│
├── High confidence (≥0.95) → Direct output
│
…