Regex vs LLM Structured Text provides a practical decision framework for choosing between regular expressions and language models when parsing structured text such as quizzes, forms, and invoices. The core insight is to let regex handle the vast majority of predictable patterns cheaply and deterministically, while reserving expensive language model calls exclusively for low-confidence edge cases. It guides you through setting up a hybrid parsing pipeline complete with confidence scoring and validation checks. Reach for this skill when you need to optimize cost and accuracy trade-offs in text processing pipelines.