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+---
+title: Your First LLM Extraction with Instructor
+description: Step-by-step tutorial for your first structured data extraction from language models using Instructor and Pydantic.
+---
+
+# Your First LLM Extraction: Structured Outputs Tutorial
+
+Learn how to extract structured data from LLMs using Instructor in this hands-on tutorial. We'll build a simple yet powerful example that demonstrates how to transform unstructured text into validated Python objects using GPT-4, Claude, or any supported LLM.
+
+## Quick Start: Extract Structured Data from LLMs
+
+This LLM tutorial shows you how to extract structured information from natural language. We'll parse a person's name and age - a perfect starting point for understanding Instructor's power:
+
+```python
+from pydantic import BaseModel
+import instructor
+# 1. Define your data model for LLM extraction
+class Person(BaseModel):
+    name: str
+    age: int
+
+# 2. Initialize Instructor with your LLM provider
+client = instructor.from_provider("openai/gpt-5-nano")
+
+# 3. Extract structured data from LLM
+person = client.create(
+    response_model=Person,   # Type-safe extraction
+    messages=[
+        {"role": "user", "content": "John Doe is 30 years old"}
+    ]
+)
+
+# 4. Use validated, structured data from LLM
+print(f"Name: {person.name}, Age: {person.age}")
+# Output: Name: John Doe, Age: 30
+```
+
+## How Instructor LLM Extraction Works
+
+```
+┌─────────────┐    ┌──────────────┐    ┌─────────────┐
+│ Define      │ -> │ Instruct LLM │ -> │ Get Typed   │
+│ Structure   │    │ to Extract   │    │ Response    │
+└─────────────┘    └──────────────┘    └─────────────┘
+```
+
+Understanding the LLM structured output pipeline:
+
+### Step 1: Define Your LLM Output Schema
+
+```python
+class Person(BaseModel):
+    name: str
+    age: int
+```
+
+Pydantic models define the structure for LLM outputs:
+- `name`: String field for extracting names from LLM
+- `age`: Integer field with automatic type validation
+
+### Step 2: Configure Your LLM Client
+
+```python
+client = instructor.from_provider("openai/gpt-5-nano")
+```
+
+Instructor enhances your LLM client with structured output capabilities. Works with OpenAI, Anthropic, Google, and 15+ providers.
+
+### Step 3: Execute LLM Extraction
+
+```python
+person = client.create(
+    response_model=Person,
+    messages=[
+        {"role": "user", "content": "John Doe is 30 years old"}
+    ]
+)
+```
+
+Key parameters for structured LLM outputs:
+- `response_model`: Pydantic model for type-safe extraction
+- `messages`: Input text for the LLM to process
+
+Note: The model is already specified when creating the client with `from_provider()`, so you don't need to pass it again.
+
+### Step 4: Work with Validated LLM Data
+
+```python
+print(f"Name: {person.name}, Age: {person.age}")
+```
+
+Get back a fully validated Python object from your LLM - no JSON parsing, no validation errors, just clean data ready to use.
+
+## Enhance LLM Extraction with Field Descriptions
+
+Improve LLM accuracy by providing clear field descriptions:
+
+```python
+from pydantic import BaseModel, Field
+
+class Person(BaseModel):
+    name: str = Field(description="Person's full name")
+    age: int = Field(description="Person's age in years")
+```
+
+Field descriptions act as prompts, guiding the LLM to extract exactly what you need.
+
+## Handle Optional Data in LLM Responses
+
+Real-world LLM extractions often have missing data. Handle it gracefully:
+
+```python
+from typing import Optional
+
+class Person(BaseModel):
+    name: str
+    age: Optional[int] = None  # Now age is optional
+```
+
+## Continue Your LLM Tutorial Journey
+
+You've successfully extracted structured data from an LLM! Next steps:
+
+1. **[Advanced Response Models](response_models.md)** - Complex schemas for LLM outputs
+2. **[Multi-Provider Setup](../../concepts/from_provider.md)** - Use GPT-4, Claude, Gemini interchangeably
+3. **[Production Patterns](../patterns/simple_object.md)** - Real-world LLM extraction examples
+
+## Common LLM Extraction Patterns
+
+- **Entity Extraction**: Names, dates, locations from unstructured text
+- **Sentiment Analysis**: Structured sentiment scores with reasoning
+- **Data Classification**: Categorize text into predefined schemas
+- **Information Parsing**: Convert documents into structured databases
+
+Ready to build more complex LLM extractions? Continue to [Response Models](response_models.md) →