5 Prompt Patterns That Actually Work in Production

After building AI agents that process thousands of requests daily, I have learned that the difference between a prompt that works in a demo and one that works in production comes down to specific, repeatable patterns.

Here are the 5 patterns that have survived real-world testing:

1. The Constraint-First Pattern

Start with what the AI should NOT do before what it should do.

You are a code reviewer. 
NEVER approve code that:
- Has SQL injection vulnerabilities
- Contains hardcoded credentials
- Missing error handling

Then review the following code for:
- Performance issues
- Code style violations

Why it works: LLMs are completion engines. By defining boundaries first, you guide attention toward what matters within safe limits.

2. The Persona-Context-Action (PCA) Pattern

Structure every prompt with these three elements:

Persona: [Expert role with specific background]
Context: [Situation, constraints, audience]  
Action: [Specific deliverable with format]

Example:
Persona: Senior backend engineer with 10 years experience
Context: Microservices architecture review for a fintech startup
Action: Provide a technical assessment in table format

Why it works: The persona primes domain knowledge, context sets boundaries, and action makes output actionable.

3. The Chain-of-Verification Pattern

For outputs where accuracy is critical, explicitly require self-checking:

Explain [concept]. Then:
1. State each claim as a numbered list
2. For each claim, cite a source or mark "unverified"
3. Flag any assumptions explicitly

Why it works: This forces the model to evaluate its own output rather than just generating plausible text.

4. The Negative Example Pattern

Show what bad output looks like, not just good output:

Write a PR description.

GOOD example:
"Refactors user authentication to use JWT tokens. 
- Adds JWT middleware
- Updates login/logout handlers
- Maintains backward compatibility"

BAD example:
"Fixed authentication"

Now write a PR description for: [your PR]

Why it works: Negative examples anchor the model understanding of quality more precisely than abstract instructions.

5. The Output Scaffold Pattern

Provide a template that the output must fill:

Analyze this error log and provide recommendations.

Use this structure:
## Root Cause
[Your analysis]

## Impact
- User-facing: [description]
- System: [description]

## Recommended Fix
1. [step]
2. [step]

## Priority: [Critical/High/Medium/Low]

---
Error log:
[paste your log]

Why it works: Scaffolding reduces variability and ensures consistent, actionable outputs.

Which Pattern Should You Use?

Use Case	Best Pattern
Code generation	Constraint-First + Chain-of-Verification
Analysis/review	PCA + Output Scaffold
Content creation	Negative Example + Output Scaffold
Question answering	Chain-of-Verification
Classification	PCA + Output Scaffold

The key insight: production prompts are not about being clever—they are about being systematic. Pick a pattern, apply it consistently, and iterate based on real outputs.

What is your favorite prompt pattern? Drop it in the comments.