Provides decision framework and Python code for hybrid regex-LLM parsing of structured text like quizzes, forms, and invoices—prioritize regex, LLM for edge cases.
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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)
│
▼
[Text Cleaner] ─── Removes noise (markers, page numbers, artifacts)
│
▼
[Confidence Scorer] ─── Flags low-confidence extractions
│
├── High confidence (≥0.95) → Direct output
│
└── Low confidence (<0.95) → [LLM Validator] → Output
import re
from dataclasses import dataclass
@dataclass(frozen=True)
class ParsedItem:
id: str
text: str
choices: tuple[str, ...]
answer: str
confidence: float = 1.0
def parse_structured_text(content: str) -> list[ParsedItem]:
"""Parse structured text using regex patterns."""
pattern = re.compile(
r"(?P<id>\d+)\.\s*(?P<text>.+?)\n"
r"(?P<choices>(?:[A-D]\..+?\n)+)"
r"Answer:\s*(?P<answer>[A-D])",
re.MULTILINE | re.DOTALL,
)
items = []
for match in pattern.finditer(content):
choices = tuple(
c.strip() for c in re.findall(r"[A-D]\.\s*(.+)", match.group("choices"))
)
items.append(ParsedItem(
id=match.group("id"),
text=match.group("text").strip(),
choices=choices,
answer=match.group("answer"),
))
return items
Flag items that may need LLM review:
@dataclass(frozen=True)
class ConfidenceFlag:
item_id: str
score: float
reasons: tuple[str, ...]
def score_confidence(item: ParsedItem) -> ConfidenceFlag:
"""Score extraction confidence and flag issues."""
reasons = []
score = 1.0
if len(item.choices) < 3:
reasons.append("few_choices")
score -= 0.3
if not item.answer:
reasons.append("missing_answer")
score -= 0.5
if len(item.text) < 10:
reasons.append("short_text")
score -= 0.2
return ConfidenceFlag(
item_id=item.id,
score=max(0.0, score),
reasons=tuple(reasons),
)
def identify_low_confidence(
items: list[ParsedItem],
threshold: float = 0.95,
) -> list[ConfidenceFlag]:
"""Return items below confidence threshold."""
flags = [score_confidence(item) for item in items]
return [f for f in flags if f.score < threshold]
def validate_with_llm(
item: ParsedItem,
original_text: str,
client,
) -> ParsedItem:
"""Use LLM to fix low-confidence extractions."""
response = client.messages.create(
model="claude-haiku-4-5-20251001", # Cheapest model for validation
max_tokens=500,
messages=[{
"role": "user",
"content": (
f"Extract the question, choices, and answer from this text.\n\n"
f"Text: {original_text}\n\n"
f"Current extraction: {item}\n\n"
f"Return corrected JSON if needed, or 'CORRECT' if accurate."
),
}],
)
# Parse LLM response and return corrected item...
return corrected_item
def process_document(
content: str,
*,
llm_client=None,
confidence_threshold: float = 0.95,
) -> list[ParsedItem]:
"""Full pipeline: regex -> confidence check -> LLM for edge cases."""
# Step 1: Regex extraction (handles 95-98%)
items = parse_structured_text(content)
# Step 2: Confidence scoring
low_confidence = identify_low_confidence(items, confidence_threshold)
if not low_confidence or llm_client is None:
return items
# Step 3: LLM validation (only for flagged items)
low_conf_ids = {f.item_id for f in low_confidence}
result = []
for item in items:
if item.id in low_conf_ids:
result.append(validate_with_llm(item, content, llm_client))
else:
result.append(item)
return result
From a production quiz parsing pipeline (410 items):
| Metric | Value |
|---|---|
| Regex success rate | 98.0% |
| Low confidence items | 8 (2.0%) |
| LLM calls needed | ~5 |
| Cost savings vs all-LLM | ~95% |
| Test coverage | 93% |