Free-form text is not data

The first time you ask Claude to "extract the user's email from this support ticket," it returns exactly what you wanted: maya@promptdojo.dev. The second time, it returns Sure! The email is maya@promptdojo.dev.. The third time, The email address you're looking for is maya@promptdojo.dev.

Three different shapes. Your downstream code expected a string. Now it either has to regex-extract the email out of natural language every time, or fail.

This is why every production AI feature — without exception — uses structured output: you tell the model exactly what JSON shape to return, and you validate the response when it comes back.

The pattern AI ships every time:

import anthropic
from pydantic import BaseModel

class Ticket(BaseModel):
    email: str
    severity: int          # 1-5
    summary: str

client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=512,
    messages=[{"role": "user", "content": "Extract from: <ticket text>"}],
)

raw = response.content[0].text
ticket = Ticket.model_validate_json(raw)   # parse + validate in one call
print(ticket.email)

Three pieces. The schema (the BaseModel class), the prompt (asking for JSON), and the validation step (model_validate_json). All three matter. Skip the schema and you're back to regex-on-natural-language. Skip the validation and you'll find out about the model's hallucinated field at 3am from a NoneType error in production.

Browser note: Pydantic isn't bundled with Pyodide. We'll use plain dict validation here — same logic, just spelled out — so you can read and write the pattern. Switching to BaseModel later is a two-line change.

Where AI specifically gets this wrong

• Trusting the model on first try. Models lie. They drop required fields, return strings where you wanted ints, and invent enum values you never defined. Validate every response.
• Forgetting response_format / tool use. On OpenAI, the modern canonical way is Structured Outputs: response_format={"type": "json_schema", "json_schema": {...}} — the API guarantees the response conforms to your schema. The older {"type": "json_object"} mode only guarantees valid JSON, not your shape. On Anthropic you typically use a tool definition (or the newer output_format parameter). Without one, the model wraps its JSON in prose.
• Catching ValidationError too broadly. When Pydantic rejects a response, you usually want to retry with the error message back to the model — not silently fall through.

Run the editor. We extract a name and validate the shape by hand.