regex-vs-llm-structured-text

Topics in the source

• Regex vs LLM for Structured Text Parsing
• When to Activate
• Decision Framework
• Architecture Pattern
• Implementation
• 1. Regex Parser (Handles the Majority)
• 2. Confidence Scoring
• 3. LLM Validator (Edge Cases Only)

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. 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 Flag items that may need LLM review: From a production quiz parsing pipeline (410 items): | Metric | Value | |--------|-------| | Regex success rate | 98.0% | | Low confidence items | 8 (2.0%) | | LLM calls needed | 5 | | Cost savings vs all-LLM | 95% | | Test coverage | 93% | Start with regex — even imperfect regex gives you a baseline to improve Use confidence scoring to programmatically identify what needs LLM help Use the cheapest LLM for validation (Haiku-class models are sufficient) Never mutate parsed items — return new instances from cleaning/validation steps TDD works well for parsers — write tests for known patterns first, then edge cases Log metrics (regex success rate, LLM call count) to track pipeline health

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