Schema-Driven Data Extraction
Providing JSON schemas in prompts to guarantee structured output format.
Why Schema-Driven Extraction?
When you tell a model extract the important data, you get inconsistent, unpredictable output. When you provide a JSON schema and say extract data matching this exact schema, you get machine-readable, consistent, type-safe output every time.
Schema-driven extraction is the pattern used in production systems that process invoices, contracts, medical records, meeting notes, and any document where structured data must be reliably extracted from unstructured text.
Providing the Schema in the Prompt
The schema lives directly in the prompt. The model uses it as the output contract:
import anthropic, json
client = anthropic.Anthropic(api_key='YOUR_API_KEY')
INVOICE_SCHEMA = '''
{
"invoice_number": "string",
"vendor_name": "string",
"vendor_address": "string or null",
"invoice_date": "YYYY-MM-DD",
"due_date": "YYYY-MM-DD or null",
"line_items": [
{
"description": "string",
"quantity": "number",
"unit_price": "number",
"total": "number"
}
],
"subtotal": "number",
"tax": "number or null",
"total_amount": "number",
"currency": "3-letter ISO code e.g. USD"
}
'''
def extract_invoice(invoice_text):
prompt = f'Extract structured data from this invoice.\nReturn JSON matching this schema exactly:\n{INVOICE_SCHEMA}\n\nInvoice:\n{invoice_text}'
r = client.messages.create(model='claude-opus-4-5', max_tokens=500, messages=[{'role': 'user', 'content': prompt}])
return json.loads(r.content[0].text)
print('Invoice schema defined.')All lessons in this course
- Named Entity Extraction Prompts
- Schema-Driven Data Extraction
- LLM as Text Classifier
- Confidence and Uncertainty in Classification