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regex-vs-llm-structured-text

Parses structured text with regex and routes low-confidence extractions to LLM validation.

1|Updated Oct 11, 2025
Search Tags:#data extraction#llm#cost optimization#regex#confidence scoring#text parsing#hybrid pipeline
One-click install
npx skills add https://github.com/ibytechaos/claude --skill regex-vs-llm-structured-text-ibytechaos
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: regex-vs-llm-structured-text
Source: https://github.com/ibytechaos/claude/tree/main/plugins/everything-claude-code/skills/regex-vs-llm-structured-text
Command: npx skills add https://github.com/ibytechaos/claude --skill regex-vs-llm-structured-text-ibytechaos

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Parsing structured text like quizzes, forms, and invoices often leads to an expensive all-LLM approach or a brittle all-regex approach. This Skill provides a decision framework and hybrid pipeline that uses regex for the 95-98% of cases it handles deterministically, reserving costly LLM calls only for low-confidence edge cases. ## Core Features & Use Cases - Decision Framework: A clear flowchart for choosing between regex, LLM, or a hybrid approach based on text consistency. - Hybrid Pipeline Architecture: Regex parser, text cleaner, confidence scorer, and LLM validator working together with a 0.95 confidence threshold. - Cost Optimization: Uses the cheapest model tier (Haiku-class) for validation only, achieving roughly 95% cost savings versus all-LLM parsing. - Use Case: Parsing 410 quiz questions from a document — regex extracts 98% correctly, confidence scoring flags 8 items, and only about 5 LLM calls are needed to fix the edge cases. ## Quick Start Ask the AI to help you build a parser for your structured document using regex first, with confidence scoring and LLM fallback only for low-confidence items.

Frequently Asked Questions about regex-vs-llm-structured-text

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I parse structured text with regex in Python?▼

Use re.compile with named groups and MULTILINE/DOTALL flags to match repeating patterns like numbered items, choices, and answers. Iterate over finditer results and build immutable dataclass instances for each parsed item.

When should I use regex vs LLM for text extraction?▼

Use regex when over 90% of the text follows a consistent repeating pattern, since it handles 95-98% of cases cheaply and deterministically. Use an LLM only for free-form, highly variable text or to validate low-confidence regex extractions.

How do I reduce LLM costs when parsing documents?▼

Run regex extraction first, then score each result's confidence and send only items below a 0.95 threshold to the LLM. Use the cheapest model tier such as Haiku-class models for validation, cutting costs by roughly 95%.

What is confidence scoring in text parsing pipelines?▼

Confidence scoring programmatically flags extractions likely to be wrong by checking signals like too few choices, missing answers, or suspiciously short text. Items scoring below the threshold are routed to an LLM validator for correction.

Why does regex parsing fail on some documents?▼

Regex fails on free-form or highly variable text without consistent repeating patterns, and on malformed input with missing fields or encoding issues. For those cases, send the text directly to an LLM instead of forcing pattern matching.

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