Prompt Structure Patterns for Production

Prompts Are Interfaces

In production systems, prompts function as interfaces between application code and AI models.

Like any interface, they must be:

• Predictable: Same input → similar output
• Testable: Outputs can be validated
• Maintainable: Easy to update without breakage
• Debuggable: Failures can be traced

Ad hoc prompting works for prototypes. Production systems need structure.

---

Pattern 1: Structured Instructions

Anti-pattern: Conversational Prompting

Write a good summary of this article that captures the main points
and makes it easy to understand what the article is about.

Problems:

• Vague (“good”, “easy to understand”)
• No output format specified
• Cannot validate programmatically

Pattern: Structured Template

Task: Summarize article
Input: {article_text}
Output format: JSON
Required fields:
- title: string (max 100 chars)
- summary: string (2-3 sentences)
- key_points: array of strings (3-5 items)

Constraints:
- Summary must be factual, no opinions
- Key points must be extracted verbatim when possible
- Do not include meta-commentary

Benefits:

• Clear expectations
• Validatable output
• Maintainable template

---

Pattern 2: Separation of Concerns

Anti-pattern: Everything in One Prompt

Read this customer email, figure out what they want, check our
knowledge base for relevant docs, write a professional response,
and make sure to mention our refund policy if needed.

Problems:

• Multiple responsibilities mixed
• Hard to debug which part failed
• Cannot optimize each step independently

Pattern: Pipeline of Focused Prompts

# Step 1: Classification
intent = classify_intent(email)  # Single-purpose prompt

# Step 2: Retrieval
docs = retrieve_docs(intent)  # Not AI, deterministic

# Step 3: Response generation
response = generate_response(
    email=email,
    intent=intent,
    context=docs
)  # Single-purpose prompt

Benefits:

• Each step testable independently
• Can optimize or replace steps
• Clear failure attribution

---

Pattern 3: Input/Output Boundaries

Anti-pattern: Ambiguous Data Flow

Here's some data about a user:
{random_json_dump}

Do something useful with it.

Pattern: Explicit Boundaries

INPUT_SCHEMA = {
    "user_id": "string",
    "purchase_history": ["product_id"],
    "preferences": {"category": "string"}
}

OUTPUT_SCHEMA = {
    "recommendations": ["product_id"],
    "reasoning": "string"
}

prompt = f"""
Task: Generate product recommendations
Input (validated): {validated_input}
Output format: {OUTPUT_SCHEMA}

Rules:
1. Recommend 3-5 products
2. Products must be from purchase history or preferred category
3. Provide brief reasoning for each recommendation
"""

Benefits:

• Input validated before sending
• Output validated before using
• Contract between code and AI

---

Pattern 4: Few-Shot Examples

Anti-pattern: Hope It Understands

Extract the customer name from this text:
{text}

Pattern: Examples as Specification

prompt = f"""
Task: Extract customer name

Example 1:
Input: "Hi, I'm Sarah Chen calling about my order"
Output: {{"name": "Sarah Chen"}}

Example 2:
Input: "This is John from accounting"
Output: {{"name": "John"}}

Example 3:
Input: "The package should go to 123 Main St"
Output: {{"name": null}}

Now process:
Input: {text}
Output:
"""

Benefits:

• Examples define expected behavior
• Edge cases documented
• Reduces ambiguity

---

Pattern 5: Constraint-First Design

Anti-pattern: Hope It Behaves

Generate a product description

Pattern: Explicit Constraints

prompt = f"""
Task: Generate product description

Input:
Product: {product_name}
Features: {features}

Constraints:
- Length: 50-100 words
- Tone: Professional, factual
- Must NOT: Use superlatives ("best", "perfect", "amazing")
- Must NOT: Make unverified claims
- Must NOT: Compare to competitors
- Must INCLUDE: At least 2 concrete features

Format: Plain text, one paragraph

Generate:
"""

Benefits:

• Behavioral boundaries clear
• Violations easier to detect
• Reduces need for post-processing

---

Pattern 6: Version Control for Prompts

Anti-pattern: Inline String Literals

# Prompt scattered across codebase
response = llm.generate(f"Summarize: {text}")

Pattern: Centralized Prompt Registry

# prompts.py
PROMPTS = {
    "summarize_v1": {
        "version": "1.0",
        "template": """
Task: Summarize text
Input: {text}
Output: JSON with title, summary (2-3 sentences)
        """,
        "created": "2026-01-15",
        "author": "engineering"
    },
    "summarize_v2": {
        "version": "2.0",
        "template": """
Task: Summarize text
Input: {text}
Output: JSON with title, summary, key_points
Constraints: Summary 2-3 sentences, factual only
        """,
        "created": "2026-02-01",
        "author": "engineering"
    }
}

# usage
prompt = PROMPTS["summarize_v2"]["template"].format(text=article)

Benefits:

• Prompt changes tracked in git
• A/B testing different versions
• Rollback capability

---

Pattern 7: Chain-of-Thought for Complex Reasoning

Anti-pattern: Expect Direct Answer

Input: Complex problem
Output: Answer

Pattern: Structured Reasoning

prompt = f"""
Problem: {complex_problem}

Solve step-by-step:
1. Restate the problem in your own words
2. Identify key information
3. Outline your approach
4. Show your work
5. State your final answer

Format:
Problem Understanding: ...
Key Information: ...
Approach: ...
Work: ...
Final Answer: ...
"""

Benefits:

• Improves accuracy on complex tasks
• Reasoning is inspectable
• Failures easier to debug

---

Pattern 8: Defensive Prompting

Anti-pattern: Assume Perfect Input

Translate this text: {user_input}

Pattern: Handle Edge Cases

prompt = f"""
Task: Translate text to Spanish

Input: {user_input}

Before translating:
1. If input is empty, return: {{"error": "empty_input"}}
2. If input is not a valid language, return: {{"error": "invalid_input"}}
3. If input is already Spanish, return original text

Translation:
"""

Benefits:

• Graceful degradation
• Fewer unexpected failures
• Better error messages

---

Pattern 9: Metadata for Debugging

Anti-pattern: Output Only

Result: The summary text

Pattern: Output + Metadata

OUTPUT_SCHEMA = {
    "result": "string",
    "metadata": {
        "model": "string",
        "timestamp": "string",
        "input_tokens": "int",
        "output_tokens": "int",
        "confidence": "string"
    }
}

prompt = f"""
Task: Summarize text
Input: {text}

Output format:
{{
    "result": "your summary here",
    "metadata": {{
        "confidence": "high|medium|low",
        "flags": ["warning1", "warning2"]
    }}
}}
"""

Benefits:

• Self-reporting confidence
• Debugging information embedded
• Monitoring-friendly

---

Production Checklist

Before deploying a prompt:

• Structure: Uses clear sections (Task, Input, Output, Constraints)
• Schema: Output format is specified and validatable
• Examples: Includes 2-3 few-shot examples for complex tasks
• Constraints: Behavioral boundaries explicitly stated
• Error handling: Edge cases addressed
• Version controlled: Stored in centralized registry
• Tested: Has automated tests with expected outputs
• Logged: Includes request ID for debugging

---

Common Mistakes

❌ Clever wording instead of structure

Why it fails: Wording is fragile, structure is stable

❌ Single mega-prompt for everything

Why it fails: Cannot debug, optimize, or test components

❌ No output format specified

Why it fails: Downstream code breaks on unexpected formats

❌ Prompts scattered as string literals

Why it fails: Cannot track changes, A/B test, or rollback

---

Conclusion

Production prompts are not creative writing—they are interface contracts.

Key principles:

1. Use structure over clever wording
2. Separate concerns into focused prompts
3. Validate inputs and outputs
4. Version control prompt changes
5. Test with expected outputs

When prompts are treated as first-class interfaces, AI systems become more reliable, debuggable, and maintainable.