Tips and Tricks #158: Implement Prompt Templates for Consistent LLM Output

Use structured prompt templates to get reliable, formatted responses from LLMs.

Code Snippet

from string import Template

# Define reusable prompt template
CODE_REVIEW_TEMPLATE = Template("""
You are a senior software engineer reviewing code.

## Code to Review:
```$language
$code
```

## Review Guidelines:
1. Check for bugs and edge cases
2. Evaluate code style and readability
3. Suggest performance improvements
4. Note any security concerns

## Output Format:
Provide your review as JSON with this structure:
{
    "bugs": ["list of potential bugs"],
    "style_issues": ["list of style concerns"],
    "performance": ["list of optimization suggestions"],
    "security": ["list of security concerns"],
    "overall_score": 1-10
}
""")

def review_code(code: str, language: str) -> str:
    prompt = CODE_REVIEW_TEMPLATE.substitute(
        language=language,
        code=code
    )
    # Send to LLM...
    return call_llm(prompt)

Why This Helps

• Consistent output format across calls
• Easier to parse and process responses
• Reusable across different inputs

How to Test

• Verify JSON output parses correctly
• Test with various code samples

When to Use

Any LLM integration requiring structured output. APIs, automation, data extraction.

Performance/Security Notes

Consider using Pydantic for output validation. Add few-shot examples for complex formats.

References

• https://www.promptingguide.ai/techniques/structured

Try this tip in your next project and share your results in the comments!