Zero-Shot Extractor: Data Extraction That Actually Understands Context

Extracting structured data from documents without writing regex was my goal. Zero-Shot Extractor promised "just say what you want" extraction, but it would miss fields, extract wrong data types, or fail on edge cases. Here's how I got reliable zero-shot extraction.

Extracted Wrong Fields or Missed Data Altogether

Problem

Asked to extract "contract amount and expiration date", but extractor would get the amount wrong or miss the date entirely. Accuracy around 70%.

Actual Fix

Used few-shot prompting with examples and schema validation. Providing 2-3 examples of correct extraction improved accuracy to 95%.

Dates Extracted in Inconsistent Formats

Problem

Dates came out as "Jan 5, 2024", "01/05/2024", "January 5th, 2024" - all different formats.

Actual Fix

Pydantic models with date validators and explicit format instructions in prompts. All dates now normalized to ISO 8601.

Production Setup

from zero_shot_extractor import Extractor
from pydantic import BaseModel

class ContractData(BaseModel):
    amount: float
    expiration_date: str
    parties: list[str]

extractor = Extractor(
    model="gpt-4o",
    examples=[{
        "text": "Contract between ABC Corp and XYZ Inc for $50,000 expiring Dec 31, 2024",
        "output": {"amount": 50000, "expiration_date": "2024-12-31", "parties": ["ABC Corp", "XYZ Inc"]}
    }]
)

result = extractor.extract("Your contract text here", schema=ContractData)

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