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+---
+draft: False
+date: 2024-03-08
+authors:
+  - jxnl
+---
+
+# Simple Synthetic Data Generation
+
+What that people have been using instructor for is to generate synthetic data rather than extracting data itself. We can even use the J-Schemo extra fields to give specific examples to control how we generate data. 
+
+Consider the example below. We'll likely generate very simple names.
+
+```python
+from typing import Iterable
+from pydantic import BaseModel
+import instructor
+from openai import OpenAI
+
+
+# Define the UserDetail model
+class UserDetail(BaseModel):
+    name: str
+    age: int
+
+
+# Patch the OpenAI client to enable the response_model functionality
+client = instructor.patch(OpenAI())
+
+
+def generate_fake_users(count: int) -> Iterable[UserDetail]:
+    return client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        response_model=Iterable[UserDetail],
+        messages=[
+            {"role": "user", "content": f"Generate a {count} synthetic users"},
+        ],
+    )
+
+
+for user in generate_fake_users(5):
+    print(user)
+    """
+    name='Alice' age=25
+    name='Bob' age=30
+    name='Charlie' age=35
+    name='David' age=40
+    name='Eve' age=45
+    """
+```
+
+## Leveraging Simple Examples
+
+We might want to set examples as part of the prompt by leveraging Pydantics configuration. We can set examples directly in the JSON scheme itself.
+
+```python
+from typing import Iterable
+from pydantic import BaseModel
+import instructor
+from openai import OpenAI
+
+
+# Define the UserDetail model
+class UserDetail(BaseModel):
+    name: str = Field(examples=["Timothee Chalamet", "Zendaya"])
+    age: int
+
+
+# Patch the OpenAI client to enable the response_model functionality
+client = instructor.patch(OpenAI())
+
+
+def generate_fake_users(count: int) -> Iterable[UserDetail]:
+    return client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        response_model=Iterable[UserDetail],
+        messages=[
+            {"role": "user", "content": f"Generate a {count} synthetic users"},
+        ],
+    )
+
+
+for user in generate_fake_users(5):
+    print(user)
+    """
+    name='Timothee Chalamet' age=25
+    name='Zendaya' age=24
+    name='Keanu Reeves' age=56
+    name='Scarlett Johansson' age=36
+    name='Chris Hemsworth' age=37
+    """
+```
+
+By incorporating names of celebrities as examples, we have shifted towards generating synthetic data featuring well-known personalities, moving away from the simplistic, single-word names previously used.
+
+## Leveraging Complex Example
+
+To effectively generate synthetic examples with more nuance, lets upgrade to the "gpt-4-turbo-preview" model, use model level examples rather than attribute level examples:
+
+```Python
+import instructor
+
+from typing import Iterable
+from pydantic import BaseModel, Field, ConfigDict
+from openai import OpenAI
+
+
+# Define the UserDetail model
+class UserDetail(BaseModel):
+    """Old Wizards"""
+    name: str
+    age: int
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "examples": [
+                {"name": "Gandalf the Grey", "age": 1000},
+                {"name": "Albus Dumbledore", "age": 150},
+            ]
+        }
+    )
+
+
+# Patch the OpenAI client to enable the response_model functionality
+client = instructor.patch(OpenAI())
+
+
+def generate_fake_users(count: int) -> Iterable[UserDetail]:
+    return client.chat.completions.create(
+        model="gpt-4-turbo-preview",
+        response_model=Iterable[UserDetail],
+        messages=[
+            {"role": "user", "content": f"Generate `{count}` synthetic examples"},
+        ],
+    )
+
+
+for user in generate_fake_users(5):
+    print(user)
+    """
+    name='Merlin' age=196
+    name='Saruman the White' age=543
+    name='Radagast the Brown' age=89
+    name='Morgoth' age=901
+    name='Filius Flitwick' age=105 
+    """
+```
+
+## Leveraging Descriptions
+
+By adjusting the descriptions within our Pydantic models, we can subtly influence the nature of the synthetic data generated. This method allows for a more nuanced control over the output, ensuring that the generated data aligns more closely with our expectations or requirements. 
+
+For instance, specifying "Fancy French sounding names" as a description for the `name` field in our `UserDetail` model directs the generation process to produce names that fit this particular criterion, resulting in a dataset that is both diverse and tailored to specific linguistic characteristics.
+
+
+```python
+import instructor
+
+from typing import Iterable
+from pydantic import BaseModel, Field
+from openai import OpenAI
+
+
+# Define the UserDetail model
+class UserDetail(BaseModel):
+    name: str = Field(description="Fancy French sounding names")
+    age: int
+
+
+# Patch the OpenAI client to enable the response_model functionality
+client = instructor.patch(OpenAI())
+
+
+def generate_fake_users(count: int) -> Iterable[UserDetail]:
+    return client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        response_model=Iterable[UserDetail],
+        messages=[
+            {"role": "user", "content": f"Generate `{count}` synthetic users"},
+        ],
+    )
+
+
+for user in generate_fake_users(5):
+    print(user)
+    """
+    name='Jean' age=25
+    name='Claire' age=30
+    name='Pierre' age=22
+    name='Marie' age=27
+    name='Luc' age=35
+    """
+```
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