Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.

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 Architecture Pattern Implementation 1. Regex Parser (Handles the Majority) 2. Confidence Scoring Flag items that may need LLM review: 3. LLM Validator (Edge Cases Only) 4. Hybrid Pipeline Real World Metrics 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% Best Practices 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 Anti Patterns to Avoid Sending all text to an LLM when regex handles 95%+ of cases (expensive and slow) Using regex for free form, highly variable text (LLM is better here) Skipping confidence scoring and hoping regex "just works" Mutating parsed objects during cleaning/validation steps Not testing edge cases (malformed input, missing fields, encoding issues) When to Use Quiz/exam question parsing Form data extraction Invoice/receipt processing Document structure parsing (headers, sections, tables) Any structured text with repeating patterns where cost matters

regex-vs-llm-structured-text