regex-vs-llm-structured-text

Automate regex parsing and LLM validation for structured text extraction.

16|3|Updated Mar 12, 2026

Search Tags:#llm #edge-cases #regex #parsing #confidence-scoring #structured-text #hybrid-pipeline

Ready to run

npx skills add https://github.com/sehoon787/my-claude --skill regex-vs-llm-structured-text-sehoon787

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: regex-vs-llm-structured-text
Source: https://github.com/sehoon787/my-claude/tree/main/skills/ecc/regex-vs-llm-structured-text
Command: npx skills add https://github.com/sehoon787/my-claude --skill regex-vs-llm-structured-text-sehoon787

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Parses structured text efficiently by starting with deterministic regex extraction and reserving expensive steps for edge cases, reducing cost and latency.

Core Features & Use Cases

Regex-first parsing: reliably extract structured items (ids, questions, options, answers) from consistent formats.
Confidence scoring: automatically flag uncertain extractions for review.
LLM validation for edge cases: selectively validate and correct low-confidence items using an LLM.
Use Case: quizzes, forms, invoices, and other repeating-structure documents.

Quick Start

Provide a sample structured text and ask the system to extract questions, choices, and answers using the framework.

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 from invoices and forms without high LLM costs?▼

Hybrid parsing applies deterministic regex extraction to repeating patterns in quizzes, forms, and invoices, reserving LLM validation only for low-confidence edge cases to reduce cost and latency.

What is the best way to validate uncertain regex extraction results for edge cases?▼

Use a confidence scoring system that automatically flags uncertain regex extractions, selectively routing only those low-confidence items to an LLM validator for correction and review.

How does confidence scoring work when extracting repeating patterns from documents?▼

Confidence scoring evaluates regex extraction results from repeating document patterns, automatically flagging uncertain items so the LLM validator selectively processes only those edge cases needing correction.

Can I use regex and LLM together in a hybrid pipeline for structured text parsing?▼

Yes, a hybrid pipeline applies regex-first parsing to reliably extract IDs, questions, and options from consistent formats, then invokes an LLM validator to selectively process low-confidence edge cases.

When should I avoid deterministic regex parsing and invoke AI for structured text?▼

Avoid strict deterministic regex parsing and invoke AI when the confidence scoring system flags edge cases as low-confidence, indicating the structured text format varies too widely for reliable regex extraction.