Tips and Tricks – Implement Prompt Templates for Consistent LLM Output

Inconsistent LLM outputs plague production AI applications.
The same prompt can return dramatically different formats, structures, and quality levels. Prompt templates solve
this by standardizing how you communicate with LLMs, ensuring consistent, predictable, and production-ready outputs
every time.

This guide covers battle-tested prompt template patterns
used in production systems processing millions of LLM requests. We’ll explore template design, variable injection,
validation, and real-world implementations.

Why Prompt Templates Matter

The Problem with Ad-Hoc Prompts

Without templates, teams face:

Inconsistent outputs: Different engineers write different prompts for similar tasks
Quality variance: No standardized quality checks or constraints
Maintenance nightmare: Scattered prompts across codebase
No version control: Can’t track what prompts produced what results
Difficult testing: Hard to test prompts systematically

Template Benefits

Consistency: Same structure every time
Reusability: Write once, use everywhere
Version control: Track changes to prompts
Testability: Easy to unit test
Collaboration: Team shares best practices

Pattern 1: Basic String Templates

Python with f-strings

# Simple but effective prompt templates
def create_summary_prompt(text: str, max_words: int = 100) -> str:
    template = f"Summarize the following text in {max_words} words or less.\n"
    template += "Focus on key points and maintain accuracy.\n\n"
    template += f"Text:\n{text}\n\nSummary:"
    return template

def create_classification_prompt(text: str, categories: list[str]) -> str:
    categories_formatted = ", ".join(categories)
    template = f"Classify the following text into one of these categories: {categories_formatted}\n\n"
    template += f"Text: {text}\n\nCategory:"
    return template

# Usage
text = "Long article about AI trends in 2025..."
prompt = create_summary_prompt(text, max_words=50)
response = llm.generate(prompt)

When to Use Basic Templates

Simple, single-purpose prompts
Low-complexity variable injection
Rapid prototyping
Small teams with simple needs

Pattern 2: LangChain PromptTemplate

Production-Grade Templates

from langchain.prompts import PromptTemplate, ChatPromptTemplate
from langchain.prompts.chat import SystemMessage, HumanMessage

# Simple text template
summary_template = PromptTemplate(
    input_variables=["text", "max_words", "style"],
    template="Create a {style} summary in {max_words} words.\n\nText: {text}\n\nSummary:"
)

# Chat template for conversational AI
chat_template = ChatPromptTemplate.from_messages([
    SystemMessage(content="You are an expert {domain} assistant."),
    HumanMessage(content="Context: {context}\n\nQuestion: {question}")
])

# Usage
prompt = summary_template.format(
    text="Long article...",
    max_words=100,
    style="technical"
)

Advanced Features

Variable validation: Ensures all variables are provided
Partial formatting: Fill some variables now, others later
Output parsers: Structured output validation
Few-shot examples: Include examples in template

Pattern 3: Structured Output Templates

JSON-Formatted Responses

from langchain.prompts import PromptTemplate
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field

# Define expected output structure
class ProductAnalysis(BaseModel):
    sentiment: str = Field(description="Sentiment: positive, negative, or neutral")
    key_features: list[str] = Field(description="List of key features")
    pain_points: list[str] = Field(description="Customer pain points")
    recommendation: str = Field(description="Purchase recommendation")

# Create parser
parser = PydanticOutputParser(pydantic_object=ProductAnalysis)

# Template with format instructions
analysis_template = PromptTemplate(
    template="Analyze this product review.\n\nReview: {review}\n\n{format_instructions}",
    input_variables=["review"],
    partial_variables={"format_instructions": parser.get_format_instructions()}
)

# Usage
review = "Great laptop, but battery life could be better..."
prompt = analysis_template.format(review=review)
response = llm.generate(prompt)

# Parse structured output
try:
    result = parser.parse(response)
    print(f"Sentiment: {result.sentiment}")
    print(f"Features: {result.key_features}")
except Exception as e:
    print(f"Parsing failed: {e}")

Benefits of Structured Templates

Type safety: Pydantic validates output structure
API integration: Easy to integrate with downstream systems
Error handling: Catch malformed responses early
Documentation: Schema serves as documentation

Pattern 4: Few-Shot Templates

Learning from Examples

from langchain.prompts import FewShotPromptTemplate, PromptTemplate

# Define examples
examples = [
    {
        "input": "Customer wants refund for damaged item",
        "output": "Priority: High | Category: Refund | Action: Process immediately"
    },
    {
        "input": "General inquiry about shipping times",
        "output": "Priority: Low | Category: Info | Action: Standard response"
    }
]

# Example template
example_template = PromptTemplate(
    input_variables=["input", "output"],
    template="Input: {input}\nOutput: {output}"
)

# Few-shot template
few_shot_template = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_template,
    prefix="Classify customer support tickets into priority, category, and action.",
    suffix="Input: {input}\nOutput:",
    input_variables=["input"]
)

# Usage
new_ticket = "Charged twice for same order!"
prompt = few_shot_template.format(input=new_ticket)
response = llm.generate(prompt)

When to Use Few-Shot Templates

Complex classification tasks
Domain-specific formatting
Specialized reasoning patterns
Improving output quality

Best Practices

1. Version Control Your Templates

# templates/v1/summary.py
SUMMARY_TEMPLATE_V1 = "Summarize in {max_words} words: {text}"

# templates/v2/summary.py  
SUMMARY_TEMPLATE_V2 = "Create a {style} summary of {max_words} words.\n\nText: {text}"

# Track which version produced which results
class TemplateVersion:
    def __init__(self, version: str, template: str):
        self.version = version
        self.template = template
        self.created_at = datetime.now()
    
    def generate(self, **kwargs):
        result = llm.generate(self.template.format(**kwargs))
        # Log version used
        log_usage(version=self.version, result=result)
        return result

2. Add Input Validation

from pydantic import BaseModel, validator

class SummaryInput(BaseModel):
    text: str
    max_words: int = 100
    style: str = "technical"
    
    @validator('text')
    def text_not_empty(cls, v):
        if not v or len(v.strip()) < 10:
            raise ValueError('Text must be at least 10 characters')
        return v
    
    @validator('max_words')
    def valid_word_count(cls, v):
        if v < 10 or v > 500:
            raise ValueError('max_words must be between 10 and 500')
        return v

# Usage with validation
try:
    inputs = SummaryInput(text="Short text...", max_words=50)
    prompt = create_summary_prompt(**inputs.dict())
except ValueError as e:
    print(f"Validation error: {e}")

3. Implement Template Testing

import pytest

def test_summary_template():
    template = create_summary_prompt(
        text="AI is transforming industries",
        max_words=20
    )
    
    assert "Summarize" in template
    assert "20 words" in template
    assert "AI is transforming industries" in template

def test_template_with_mock_llm(mocker):
    mocker.patch('llm.generate', return_value="Mocked summary")
    
    result = generate_with_template(text="Test text", max_words=50)
    
    assert result == "Mocked summary"
    llm.generate.assert_called_once()

Real-World Example: Customer Support

class CustomerSupportTemplates:
    @staticmethod
    def ticket_classification(ticket: str) -> str:
        return f"Classify this support ticket.\n\nTicket: {ticket}\n\nProvide JSON:\n{{\n  \"priority\": \"low|medium|high\",\n  \"category\": \"billing|technical|account\",\n  \"requires_human\": boolean\n}}"
    
    @staticmethod
    def response_draft(ticket: str, context: str) -> str:
        return f"Draft professional response.\n\nTicket: {ticket}\n\nContext: {context}\n\nResponse:"

# Usage in production
def process_ticket(ticket_text: str):
    # Step 1: Classify
    classification_prompt = CustomerSupportTemplates.ticket_classification(ticket_text)
    classification = llm.generate(classification_prompt)
    classification_data = json.loads(classification)
    
    # Step 2: Route based on classification
    if classification_data['requires_human']:
        escalate_to_human(ticket_text)
    else:
        response_prompt = CustomerSupportTemplates.response_draft(
            ticket=ticket_text,
            context=get_customer_context()
        )
        response = llm.generate(response_prompt)
        send_response(response)

Common Pitfalls

Over-engineering: Don’t create templates for one-off prompts
Rigid templates: Allow flexibility where needed
Poor naming: Use descriptive template names
No validation: Always validate inputs and outputs
Ignoring costs: Template complexity impacts token usage

Key Takeaways

Start with simple templates, add complexity as needed
Use LangChain for production systems
Implement structured outputs for reliability
Version control your templates
Add validation and testing
Few-shot templates improve quality significantly

Prompt templates transform LLM applications from unpredictable experiments into reliable production systems. By
standardizing prompts, you gain consistency, testability, and maintainability—essential for any production AI
application.

Discover more from C4: Container, Code, Cloud & Context

Subscribe to get the latest posts sent to your email.

Searching in