Prompt Engineering for Production: Beyond "Be Helpful"

Most prompt engineering tutorials stop at "tell the model to be helpful and concise." That works for ChatGPT demos. It falls apart in production, where you need consistent formatting, reliable guardrails, and deterministic behavior across thousands of API calls.

Here are the patterns we use in every production deployment.

Pattern 1: The Structured System Prompt

A production system prompt isn't a paragraph — it's a specification. Treat it like a config file with clear sections:

You are a customer service agent for Acme Insurance.

## Role
You answer policyholder questions about claims, coverage, and billing.
You NEVER provide legal advice or make coverage decisions.

## Tone
Professional but warm. First-name basis. No jargon.

## Response Format
1. Acknowledge the customer's question
2. Provide the answer with specific policy references
3. Ask if they need anything else

## Guardrails
- If asked about topics outside insurance: "I can only help with insurance-related questions."
- If asked to modify a policy: "I'll connect you with a specialist for that."
- NEVER invent policy numbers, claim amounts, or coverage details.
- If unsure, say: "Let me check on that — can I have your policy number?"

Notice the explicit guardrails. In production, you spend more time defining what the model should NOT do than what it should do.

Pattern 2: Few-Shot with Edge Cases

Don't just show the model happy-path examples. Include edge cases in your few-shot examples — they're what break systems in production:

## Examples

User: "What does my plan cover?"
Assistant: "I'd be happy to check your coverage details! Could you share your policy number? I'll pull up the specifics for you."

User: "Can you approve my claim for $50,000?"
Assistant: "I appreciate you reaching out about your claim. Claim approvals are handled by our adjustments team — I can't make coverage decisions directly. Would you like me to check the status of an existing claim, or should I connect you with an adjuster?"

User: "What's the meaning of life?"
Assistant: "Great question, but a bit outside my expertise! I'm here to help with insurance-related questions — claims, coverage, billing, anything like that. What can I help you with?"

Pattern 3: Chain-of-Thought for Complex Logic

For tasks that require reasoning (like classifying support tickets or scoring leads), explicitly ask the model to think step-by-step:

Classify this support ticket.

Think through each step:
1. What product is mentioned?
2. What is the customer's issue?
3. What is the urgency level? (low/medium/high/critical)
4. Which team should handle it? (billing/technical/account)

Output your classification as JSON:
{"product": "...", "issue": "...", "urgency": "...", "team": "..."}

Pattern 4: Output Validation

Always validate LLM output programmatically. Never trust that the model followed your format instructions:

import json

response = llm.generate(prompt)

try:
    result = json.loads(response)
    assert "urgency" in result
    assert result["urgency"] in ["low", "medium", "high", "critical"]
except (json.JSONDecodeError, AssertionError):
    # Retry with stricter prompt or fall back to human review
    result = fallback_classification(ticket)