regex-vs-llm-structured-text

name regex-vs-llm-structured-text description Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases. metadata {"origin":"ECC"} 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 │ └── Low confidence (<0.95) → [LLM Validator] → Output Implementation 1. Regex Parser (Handles the Majority) import re from dataclasses import dataclass @dataclass( frozen= True ) class ParsedItem : id : str text: str choices: tuple [ str , ...] answer: str confidence: float = 1.0 def parse_structured_text ( content: str ) -> list [ParsedItem]: """Parse structured text using regex patterns.""" pattern = re. compile ( r"(?P<id>\d+)\.\s*(?P<text>.+?)\n" r"(?P<choices>(?:[A-D]\..+?\n)+)" r"Answer:\s*(?P<answer>[A-D])" , re.MULTILINE | re.DOTALL, ) items = [] for match in pattern.finditer(content): choices = tuple ( c.strip() for c in re.findall( r"[A-D]\.\s*(.+)" , match .group( "choices" )) ) items.append(ParsedItem( id = match .group( "id" ), text= match .group( "text" ).strip(), choices=choices, answer= match .group( "answer" ), )) return items 2. Confidence Scoring Flag items that may need LLM review: @dataclass( frozen= True ) class ConfidenceFlag : item_id: str score: float reasons: tuple [ str , ...] def score_confidence ( item: ParsedItem ) -> ConfidenceFlag: """Score extraction confidence and flag issues.""" reasons = [] score = 1.0 if len (item.choices) < 3 : reasons.append( "few_choices" ) score -= 0.3 if not item.answer: reasons.append( "missing_answer" ) score -= 0.5 if len (item.text) < 10 : reasons.append( "short_text" ) score -= 0.2 return ConfidenceFlag( item_id=item. id , score= max ( 0.0 , score), reasons= tuple (reasons), ) def identify_low_confidence ( items: list [ParsedItem], threshold: float = 0.95 , ) -> list [ConfidenceFlag]: """Return items below confidence threshold.""" flags = [score_confidence(item) for item in items] return [f for f in flags if f.score < threshold] 3. LLM Validator (Edge Cases Only) def validate_with_llm ( item: ParsedItem, original_text: str , client, ) -> ParsedItem: """Use LLM to fix low-confidence extractions.""" response = client.messages.create( model= "claude-haiku-4-5-20251001" , # Cheapest model for validation max_tokens= 500 , messages=[{ "role" : "user" , "content" : ( f"Extract the question, choices, and answer from this text.\n\n" f"Text: {original_text} \n\n" f"Current extraction: {item} \n\n" f"Return corrected JSON if needed, or 'CORRECT' if accurate." ), }], ) # Parse LLM response and return corrected item... return corrected_item 4. Hybrid Pipeline def process_document ( content: str , *, llm_client= None , confidence_threshold: float = 0.95 , ) -> list [ParsedItem]: """Full pipeline: regex -> confidence check -> LLM for edge cases.""" # Step 1: Regex extraction (handles 95-98%) items = parse_structured_text(content) # Step 2: Confidence scoring low_confidence = identify_low_confidence(items, confidence_threshold) if not low_confidence or llm_client is None : return items # Step 3: LLM validation (only for flagged items) low_conf_ids = {f.item_id for f in low_confidence} result = [] for item in items: if item. id in low_conf_ids: result.append(validate_with_llm(item, content, llm_client)) else : result.append(item) return result 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