Module 9: Production Prompt Engineering & Best Practices

You've built prompts. You've tested them. They work great... on your laptop, with the examples you chose.

But will they work in production? When thousands of users hit them with unpredictable inputs? When costs scale? When a bad output damages your brand?

This module teaches professional prompt engineering—the unglamorous but essential practices that separate hobby projects from production systems.

✅ Concept 1: Evaluating Prompt Quality

You can't improve what you don't measure. Before deploying prompts, establish clear evaluation criteria.

The 5-Criteria Rubric

How to Use the Rubric

Task-Specific Metrics

⚖️ Concept 2: LLM-as-Judge Pattern

Manual evaluation doesn't scale. Use ChatGPT itself to evaluate outputs—fast, consistent, and surprisingly accurate.

Basic LLM-as-Judge Prompt

Advanced: Pairwise Comparison

Instead of scoring individually, have ChatGPT compare two outputs—more reliable for subjective quality.

Building an Evaluation Pipeline

# Python pseudo-code for automated evaluation
def evaluate_prompt(prompt, test_cases):
    scores = []
    
    for test_input in test_cases:
        # Generate output
        output = call_chatgpt(prompt + test_input)
        
        # Evaluate with LLM-as-judge
        eval_prompt = f"""
        Evaluate this output on clarity, accuracy, completeness (1-5 each):
        Output: {output}
        Return JSON: {{"clarity": X, "accuracy": X, "completeness": X}}
        """
        
        evaluation = call_chatgpt(eval_prompt)
        scores.append(parse_json(evaluation))
    
    # Calculate statistics
    avg_score = sum(scores) / len(scores)
    return avg_score

# Test prompt v1
score_v1 = evaluate_prompt(prompt_v1, test_cases)  # 3.8/5

# Improve prompt, test v2
score_v2 = evaluate_prompt(prompt_v2, test_cases)  # 4.4/5 ✅ Better!

🔒 Concept 3: Safety, Privacy & Guardrails

Content Filtering

Prevent harmful, biased, or off-brand outputs before they reach users.

Data Privacy Checklist

Bias Detection & Mitigation

📊 Concept 4: Monitoring & Observability

Key Metrics to Track

Logging Strategy

# Comprehensive logging for production prompts
import logging
import time
import json

def call_chatgpt_with_logging(prompt, user_id, request_id):
    start_time = time.time()
    
    try:
        # Make API call
        response = openai.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": prompt}]
        )
        
        latency = time.time() - start_time
        tokens_used = response.usage.total_tokens
        cost = calculate_cost(tokens_used, "gpt-4o")
        
        # Log success
        log_data = {
            "timestamp": time.time(),
            "request_id": request_id,
            "user_id": user_id,
            "model": "gpt-4o",
            "latency_seconds": latency,
            "tokens_used": tokens_used,
            "cost_usd": cost,
            "status": "success",
            "prompt_version": "v2.3"  # Track which prompt version
        }
        logging.info(json.dumps(log_data))
        
        return response.choices[0].message.content
        
    except Exception as e:
        # Log error
        log_data = {
            "timestamp": time.time(),
            "request_id": request_id,
            "user_id": user_id,
            "status": "error",
            "error_type": type(e).__name__,
            "error_message": str(e),
            "prompt_version": "v2.3"
        }
        logging.error(json.dumps(log_data))
        raise

Alert Examples

🔄 Concept 5: Prompt Versioning & Deployment

Treat Prompts Like Code

Store prompts in version control (Git), test before deploying, and roll back if needed.

A/B Testing Prompts

Never deploy untested changes to all users. Run A/B tests to validate improvements.

# A/B testing framework
def route_to_prompt_version(user_id):
    # 80% get stable version, 20% get new version
    bucket = hash(user_id) % 100
    
    if bucket < 80:
        return load_prompt("customer_support_v2.txt")  # Control
    else:
        return load_prompt("customer_support_v3.txt")  # Test
        
# After 1 week, compare metrics:
# v2 (control): 4.4/5 quality, 2.8s latency, 1.2% error rate
# v3 (test): 4.6/5 quality, 3.2s latency, 0.9% error rate
# Conclusion: v3 is better on all metrics → rollout to 100%

Rollback Plan

⚠️ Concept 6: Handling Edge Cases

Common Edge Cases

Graceful Degradation Pattern

Timeout Handling

import asyncio

async def call_with_timeout(prompt, timeout_seconds=10):
    try:
        # Race between API call and timeout
        response = await asyncio.wait_for(
            call_chatgpt_async(prompt),
            timeout=timeout_seconds
        )
        return response
    except asyncio.TimeoutError:
        # Fallback: return cached response or error message
        logging.warning(f"Timeout after {timeout_seconds}s")
        return get_cached_response() or "Request timed out. Try a shorter query."

✅ Concept 7: Production Readiness Checklist

Before deploying prompts to production, verify every item:

🎯 Final Challenge: Production-Ready Prompt System

📚 Summary: Your Production Prompt Engineering Toolkit

🎉 Course Complete!

You've mastered ChatGPT Prompt Engineering from fundamentals to production deployment

You're now equipped to build powerful AI-driven solutions. Time to make it official! 🚀

🚀 What's Next?