Template-Based Prompt Systems #

Prompt templates provide structured frameworks for generating consistent, reusable prompts across different use cases and applications.

Why Use Prompt Templates #

Consistency: Ensure all prompts follow best practices
Reusability: Create prompts once, use them repeatedly with different inputs
Modularity: Compose complex prompts from simple building blocks
Maintenance: Update prompts in a single location
Version Control: Track changes to prompts over time

Basic Structure #

A template system typically includes:

Variables: Placeholders for dynamic content
Static Text: Consistent instructions and formatting
Conditionals: Optional sections based on context
Metadata: Information about the template purpose and usage

Implementation Patterns #

Simple Variable Substitution #

You are a helpful assistant providing information about {topic}.
The user is a {audience_type} with {expertise_level} knowledge level.
Please provide a {length} explanation about: {query}

Component-Based Templates #

Break prompts into reusable sections:

def create_prompt(components, variables):
    prompt_parts = []
    
    if "role" in components:
        prompt_parts.append(ROLE_TEMPLATES[components["role"]].format(**variables))
    
    if "task" in components:
        prompt_parts.append(TASK_TEMPLATES[components["task"]].format(**variables))
    
    if "format" in components:
        prompt_parts.append(FORMAT_TEMPLATES[components["format"]].format(**variables))
    
    return "\n\n".join(prompt_parts)

Template Libraries #

Create collections of tested, optimized templates for specific scenarios:

Classification templates
Summarization templates
Creative writing templates
Analysis templates

Advanced Template Techniques #

Context-Aware Templates #

def generate_prompt(user_query, context):
    # Select template based on query characteristics
    if contains_technical_terms(user_query):
        base_template = TECHNICAL_TEMPLATE
    elif is_creative_request(user_query):
        base_template = CREATIVE_TEMPLATE
    else:
        base_template = GENERAL_TEMPLATE
    
    # Add context-specific elements
    if context.get("user_history"):
        base_template += USER_HISTORY_TEMPLATE
    
    return base_template.format(query=user_query, **context)

Dynamic Template Selection #

Use AI to select or compose templates:

Given the following user query: "{query}"
Select the most appropriate prompt template from the following options:
1. {template_1_description}
2. {template_2_description}
3. {template_3_description}

Explain your reasoning, then output the chosen template number.

Template Management #

Testing: Evaluate template performance with different inputs
Versioning: Track template changes over time
Documentation: Document purpose, variables, and usage examples
Error Handling: Validate inputs and provide fallbacks

Code Example: Template Implementation #

class PromptTemplate:
    def __init__(self, template_string, required_variables=None):
        self.template = template_string
        self.required_variables = required_variables or []
    
    def format(self, **kwargs):
        # Validate all required variables are present
        missing = [var for var in self.required_variables if var not in kwargs]
        if missing:
            raise ValueError(f"Missing required variables: {', '.join(missing)}")
        
        # Apply the variables to the template
        return self.template.format(**kwargs)

# Example usage
summary_template = PromptTemplate(
    "Summarize the following text in {style} style with {word_count} words:\n\n{text}",
    required_variables=["text", "style", "word_count"]
)

prompt = summary_template.format(
    text="Lorem ipsum...",
    style="concise",
    word_count=100
)