Implements a cost-effective hybrid framework that prioritizes regex patterns for structured text parsing and reserves LLM validation for complex edge cases.
This skill provides a practical decision framework and architectural pattern for parsing structured text like invoices, forms, and quizzes. By prioritizing deterministic regex patterns for the majority of cases and reserving expensive LLM calls only for low-confidence edge cases, it achieves up to 95% cost savings while maintaining production-grade reliability. The skill includes ready-to-use Python implementations for regex parsing, automated confidence scoring, and hybrid pipeline orchestration, ensuring developers can balance accuracy and efficiency in high-volume data extraction tasks.
主要功能
01Hybrid pipeline for cost-efficient LLM validation
021 GitHub stars
03Real-world performance metrics and benchmarks
04Automated confidence scoring system
05Deterministic Regex-first parsing logic
06Pre-built Python implementation patterns
使用场景
01Parsing academic quiz and exam documents into structured formats
02Standardizing inconsistent form submissions into reliable JSON data
03Automating high-volume invoice and receipt data extraction
