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+---
+title: Using Prompt Templates with Instructor
+description: Learn how to create reusable prompt templates for consistent structured output extraction across different use cases.
+---
+
+# Prompt Templates
+
+This guide covers how to use prompt templates with Instructor to create reusable, parameterized prompts for structured data extraction.
+
+## Why Prompt Templates Matter
+
+Good prompts are essential for effective structured data extraction. Prompt templates help you:
+
+1. Create consistent and reusable prompts
+2. Parameterize prompts with dynamic values
+3. Separate prompt engineering from application logic
+4. Standardize prompt patterns for different use cases
+
+## Basic Prompt Templates
+
+The simplest form of a prompt template is a string with placeholders for variables:
+
+```python
+from pydantic import BaseModel, Field
+import instructor
+client = instructor.from_provider("openai/gpt-5-nano")
+
+class Person(BaseModel):
+    name: str
+    age: int
+    occupation: str
+
+# Define a template with parameters
+prompt_template = """
+Extract information about the person mentioned in the following {document_type}:
+
+{content}
+
+Please provide their name, age, and occupation.
+"""
+
+# Use the template with specific values
+document_type = "email"
+content = "Hi team, I'm introducing our new project manager, Sarah Johnson. She's 34 and has been in project management for 8 years."
+
+prompt = prompt_template.format(
+    document_type=document_type,
+    content=content
+)
+
+# Extract structured data using the formatted prompt
+response = client.create(
+    model="gpt-3.5-turbo",
+    messages=[
+        {"role": "user", "content": prompt}
+    ],
+    response_model=Person
+)
+```
+
+## Using f-strings for Simple Templates
+
+For simple cases, you can use f-strings to create prompt templates:
+
+```python
+def extract_person(content, document_type="text"):
+    prompt = f"""
+    Extract information about the person mentioned in the following {document_type}:
+
+    {content}
+
+    Please provide their name, age, and occupation.
+    """
+
+    return client.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "user", "content": prompt}
+        ],
+        response_model=Person
+    )
+
+# Use the function
+person = extract_person(
+    "According to his resume, John Smith (42) works as a software developer.",
+    document_type="resume"
+)
+```
+
+## Template Functions
+
+For more complex templates, create dedicated template functions:
+
+```python
+from typing import List, Optional
+from pydantic import BaseModel
+import instructor
+client = instructor.from_provider("openai/gpt-5-nano")
+
+class ProductReview(BaseModel):
+    product_name: str
+    rating: int
+    pros: List[str]
+    cons: List[str]
+    summary: str
+
+def create_review_extraction_prompt(
+    review_text: str,
+    product_category: str,
+    include_sentiment: bool = False
+) -> str:
+    sentiment_instruction = """
+    Also include a brief sentiment analysis of the review.
+    """ if include_sentiment else ""
+
+    return f"""
+    Extract product review information from the following {product_category} review:
+
+    {review_text}
+
+    Please identify:
+    - The name of the product being reviewed
+    - The numerical rating (1-5)
+    - A list of pros/positive points
+    - A list of cons/negative points
+    - A brief summary of the review
+    {sentiment_instruction}
+    """
+
+# Use the template function
+review_text = """
+I recently purchased the UltraSound X300 headphones, and I'm mostly satisfied.
+The sound quality is amazing and the battery lasts for days. They're also very
+comfortable to wear for long periods. However, they're a bit pricey at $299, and
+the Bluetooth occasionally disconnects. Overall, I'd give them 4 out of 5 stars.
+"""
+
+prompt = create_review_extraction_prompt(
+    review_text=review_text,
+    product_category="headphone",
+    include_sentiment=True
+)
+
+review = client.create(
+    model="gpt-3.5-turbo",
+    messages=[
+        {"role": "user", "content": prompt}
+    ],
+    response_model=ProductReview
+)
+```
+
+## Best Practices for Prompt Templates
+
+1. **Be explicit about the output format**: Clearly specify what fields you need and in what format
+2. **Use consistent language**: Maintain consistent terminology throughout the template
+3. **Keep it concise**: Avoid unnecessary verbosity that could confuse the model
+4. **Parameterize only what varies**: Only make template parameters for parts that need to change
+5. **Include examples for complex tasks**: Provide few-shot examples for more complex extractions
+6. **Test with different inputs**: Ensure your template works well with a variety of inputs
+
+## Related Resources
+
+- [Simple Object Extraction](./simple_object.md) - Extracting basic objects
+- [List Extraction](./list_extraction.md) - Working with lists of objects
+- [Optional Fields](./optional_fields.md) - Handling optional data
+- [Prompting](../../concepts/prompting.md) - General prompting concepts
+- [Templating](../../concepts/templating.md) - Advanced template techniques
+
+## Next Steps
+
+- Explore [Field Validation](./field_validation.md) for ensuring data quality
+- Try [List Extraction](./list_extraction.md) for extracting multiple items
+- Learn about [Nested Structure](./nested_structure.md) for complex data