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Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
Passed 20 of 22 test cases with the skill, and 13 of 22 without it.
and 5 more in the full report
7 fixed13 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
In a quiz parser confidence scorer, an extracted question string has a total length of 7 characters (shorter than 10 characters). What exact numerical penalty should be deducted from the base score of 1.0?
| Checked | Without skill | With skill |
|---|---|---|
| Deducts 0.2 from the confidence score | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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
Source Text
│
▼
[Regex Parser] ─── Extracts structure (95-98% accuracy)
│
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[Text Cleaner] ─── Removes noise (markers, page numbers, artifacts)
│
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[Confidence Scorer] ─── Flags low-confidence extractions
│
├── High confidence (≥0.95) → Direct output
│
└── Low confidence (<0.95) → [LLM Validator] → Output
test cases passed, vs 13 / 22 without
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