## Role
Who Claude is in this context.

## Context
What the system is, what data Claude has access to, key constraints.

## Task
What Claude must do. Be specific — avoid "help the user".

## Output Format
Exact format of the response. Include examples.

SYSTEM_PROMPT = """
You are a document classifier. Classify the document into one of: invoice, contract, receipt, other.

Examples:
Document: "Payment due by 30 days for services rendered..."
Label: invoice

Document: "This agreement is made between Party A and Party B..."
Label: contract

Document: "Thank you for your purchase. Total: $42.00"
Label: receipt
"""

SYSTEM_PROMPT = """
You are a data analyst. When answering questions about data:
1. First, identify what the question is asking
2. List the relevant data points
3. Reason through the calculation
4. State your final answer

Do not skip directly to the answer.
"""

SYSTEM_PROMPT = """
Extract the following fields from the document. Return ONLY valid JSON, no other text:

{
  "date": "YYYY-MM-DD or null",
  "amount": float or null,
  "vendor": "string or null",
  "category": "invoice|receipt|contract|other"
}
"""

from pydantic import BaseModel

class DocumentExtraction(BaseModel):
    date: str | None
    amount: float | None
    vendor: str | None
    category: str

def extract(text: str) -> DocumentExtraction:
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=256,
        system=EXTRACTION_PROMPT,
        messages=[{"role": "user", "content": text}],
    )
    return DocumentExtraction.model_validate_json(response.content[0].text)

prompts/
├── v1/
│   ├── system.md
│   └── examples.md
└── v2/
    ├── system.md
    └── examples.md

from pathlib import Path
from functools import lru_cache

@lru_cache(maxsize=None)
def load_prompt(name: str, version: str = "v1") -> str:
    path = Path("prompts") / version / f"{name}.md"
    return path.read_text(encoding="utf-8")