My Prompt System for Consistent, Structured LLM Output

The Problem with LLMs in Production

LLMs are powerful but inconsistent. Without proper prompting, you'll get different output formats, missing fields, hallucinations, and poor performance.

Here's the system we use to get reliable, structured output every time.

The Prompt Structure

Every production prompt should have: role definition, output format specification, constraints, examples, and error handling.

Role Definition

Define who the AI is acting as. For example: "You are a senior QA engineer with 10 years of experience writing Jest test cases."

Output Format

Specify the exact structure you need. Use JSON schemas, markdown templates, or code examples to show the expected format.

Constraints

Set clear boundaries:

• Maximum length requirements
• Required fields
• Format specifications
• Content guidelines

Examples

Provide 2-3 examples of perfect output. Show what "good" looks like, including edge cases and common variations.

Error Handling

Tell the AI what to do when it can't complete the task:

• Return null for unknown fields
• Add warning fields
• Never make up information

Real Results

With this system, we achieve:

• 95%+ output consistency
• Zero production failures from bad LLM output
• 10x faster iteration
• Easy debugging when issues occur

Advanced Techniques

Chain of Thought: Break complex tasks into steps

Few-Shot Learning: Show 3-5 examples of perfect and imperfect output

Temperature Control: Use 0.1-0.3 for structured output, 0.5-0.7 for creative content

Conclusion

Consistent LLM output isn't magic—it's system design. Build the right prompts, and LLMs become reliable production tools.