Migration Guide

From raw OpenAI SDK

Before:

import json
from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Extract: John is 30"}],
)

# Hope for valid JSON
try:
    data = json.loads(response.choices[0].message.content)
    name = data["name"]
    age = data["age"]
except (json.JSONDecodeError, KeyError) as e:
    # Now what?
    raise

After:

from promptguard import llm_call
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

result = llm_call(model="gpt-4o", prompt="Extract: John is 30", schema=Person)
print(result.data.name, result.data.age)
# Guaranteed valid. Auto-repaired if needed.

---

From Instructor

Before:

import instructor
from openai import OpenAI
from pydantic import BaseModel

client = instructor.from_openai(OpenAI())

class Person(BaseModel):
    name: str
    age: int

person = client.chat.completions.create(
    model="gpt-4o",
    response_model=Person,
    messages=[{"role": "user", "content": "Extract: John is 30"}],
)

After:

from promptguard import llm_call
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

result = llm_call(model="gpt-4o", prompt="Extract: John is 30", schema=Person)
person = result.data

What you gain by switching

Capability	Instructor	PromptGuard
Schema validation	Yes	Yes
Auto repair retries	1	Configurable (N)
Multi-provider	OpenAI-centric	OpenAI, Anthropic, Google, Local
Prompt versioning	No	Built-in
Regression testing	No	Built-in
CLI tooling	No	Yes
TypedDict / dataclass support	Limited	Full