15 Prompts for GPT-4: From Code Generation to System Architecture

# 15 Prompts for GPT-4: From Code Generation to System Architecture You open ChatGPT, paste a problem, and get working code. But the next day, you're stuck debugging a bug that the same model helped create. The difference between a time-saver and a time-waster? The prompt. As a developer who uses GPT-4 daily for coding, debugging, and refactoring, I’ve collected 15 battle-tested prompts that actually work. No fluff, no theory—just prompts with real examples you can copy and adapt today. ## Why Prompts Matter for Programming GPT-4 (and its successors like GPT-4o and o3-mini) can generate, debug, and refactor code with high accuracy, but only if you describe the context correctly. A vague prompt like "write a sorting function" gives you generic code. A specific prompt with constraints, edge cases, and expected output gives you production-ready logic. According to OpenAI’s own documentation (platform.openai.com/docs/guides/prompt-engineering), six key strategies improve results: write clear instructions, provide reference text, split complex tasks into simpler subtasks, give the model time to "think," use external tools, and test changes systematically. ## 15 Prompts with Real Examples ### 1. Generate a Complete Function with Edge Cases **Prompt:** ``` Write a Python function calculate_shipping_cost(weight, distance, express=False) that: - Returns a float cost. - Handles weight <= 0 (raise ValueError with message "Weight must be positive"). - Handles distance <= 0 (raise ValueError with message "Distance must be positive"). - Base cost: $5 for first 1 kg and 10 km. - Additional cost: $0.50 per additional kg, $0.10 per additional km. - Express: add $10 flat fee. - Include type hints and a docstring with examples. ``` **Why it works:** It specifies function signature, edge cases, error handling, pricing logic, and documentation requirements. The model knows exactly what to produce. **Real output (GPT-4, March 2025):** ```python def calculate_shipping_cost(weight: float, distance: float, express: bool = False) -> float: """ Calculate shipping cost based on weight and distance. Args: weight: Weight in kg. Must be positive. distance: Distance in km. Must be positive. express: If True, adds $10 flat fee. Returns: Total shipping cost as float. Raises: ValueError: If weight or distance is not positive. Examples: >>> calculate_shipping_cost(1, 10) 5.0 >>> calculate_shipping_cost(2.5, 20) 5.0 + 0.75 + 1.0 = 6.75 """ if weight <= 0: raise ValueError("Weight must be positive") if distance <= 0: raise ValueError("Distance must be positive") base = 5.0 extra_weight = max(0, weight - 1) * 0.50 extra_distance = max(0, distance - 10) * 0.10 total = base + extra_weight + extra_distance if express: total += 10.0 return total ``` ### 2. Debug with Expected vs Actual Behavior **Prompt:** ``` I have this Python code that filters a list of dictionaries. Expected: return items where 'status' is 'active' and 'age' > 18. Actual: it returns all items. Find the bug and fix it. Code: data = [{'name': 'Alice', 'status': 'active', 'age': 25}, {'name': 'Bob', 'status': 'inactive', 'age': 30}] result = [item for item in data if item['status'] == 'active' if item['age'] > 18] ``` **Why it works:** You provide the code, expected output, actual output, and context. The model can isolate the issue—here, using two `if` keywords in list comprehension creates nested conditions, not an AND condition. **Fix:** Use `and` instead of two `if` statements. ### 3. Refactor for Performance or Readability **Prompt:** ``` Refactor this JavaScript function for readability and performance. It calculates Fibonacci numbers recursively. Use memoization. Add JSDoc comments. function fib(n) { if (n <= 1) return n; return fib(n-1) + fib(n-2); } ``` **Why it works:** You specify the goal (readability + performance), the technique (memoization), and documentation format (JSDoc). The model produces optimized code without changing the interface. ### 4. Generate Unit Tests from a Function **Prompt:** ``` Generate pytest unit tests for the following Python function. Include tests for normal cases, edge cases, and error cases. Use parametrize. def divide(a: float, b: float) -> float: if b == 0: raise ValueError("Division by zero") return a / b ``` **Why it works:** The prompt explicitly asks for parametrize, edge cases, and error tests. The model produces comprehensive tests. ### 5. Explain Code in Simple Terms **Prompt:** ``` Explain this Python decorator to a junior developer. Include a real-world use case (e.g., logging or timing). import functools import time def timer(func): @functools.wraps(func) def wrapper(*args, **kwargs): start = time.perf_counter() result = func(*args, **kwargs) end = time.perf_counter() print(f"{func.__name__} took {end - start:.4f}s") return result return wrapper ``` ### 6. Suggest Architectural Improvements **Prompt:** ``` I have a monolithic Flask app with 50 endpoints. Suggest a migration plan to microservices. Consider: - Current stack: Flask, PostgreSQL, Redis. - Team size: 5 developers. - Main pain points: deployment takes 30 minutes, scaling is all-or-nothing. - Suggest first service to extract (e.g., authentication or reporting). ``` ### 7. Write a SQL Query with Explanation **Prompt:** ``` Write a SQL query to find the top 5 customers by total order value in 2024, including customers who made no orders (show 0). Tables: customers(id, name), orders(id, customer_id, total, order_date). Use LEFT JOIN and COALESCE. Explain each step. ``` ### 8. Generate Regular Expressions with Test Cases **Prompt:** ``` Write a regex to validate email addresses according to RFC 5322 simplified rules: local part allows letters, digits, dots, underscores, percent, plus, and hyphens; domain part allows letters, digits, hyphens, and dots. Provide test cases that should pass and fail. ``` ### 9. Convert Code Between Languages **Prompt:** ``` Convert this Python function to TypeScript, keeping the same logic and adding proper types. def get_user_display_name(user: dict) -> str: first = user.get('first_name', '') last = user.get('last_name', '') if not first and not last: return user.get('username', 'Unknown') return f"{first} {last}".strip() ``` ### 10. Optimize Database Queries **Prompt:** ``` This Django ORM query loads 1000 objects but makes 1001 SQL queries. Optimize it to 1 query using select_related or prefetch_related. Explain the N+1 problem first. books = Book.objects.all() for book in books: print(book.author.name) ``` ### 11. Create a REST API Endpoint with Validation **Prompt:** ``` Write a Flask endpoint POST /register with JSON body {username, email, password}. Validate: username 3-20 chars alphanumeric, email valid format, password min 8 chars with one uppercase and one digit. Return 201 on success, 400 with error messages on failure. Use marshmallow for validation. ``` ### 12. Generate Documentation from Code **Prompt:** ``` Generate Markdown documentation for this Python module. Include description, installation, usage examples, and API reference for each public function. Use the docstrings. # mymodule.py def add(a: int, b: int) -> int: """Add two integers.""" return a + b def subtract(a: int, b: int) -> int: """Subtract b from a.""" return a - b ``` ### 13. Debug a Race Condition **Prompt:** ``` This Python script sometimes prints "Done" before all threads finish. Identify the race condition and fix it using threading.Event or join(). import threading import time def worker(): time.sleep(2) print("Worker done") threads = [] for _ in range(5): t = threading.Thread(target=worker) t.start() threads.append(t) print("Done") ``` ### 14. Explain a Complex Algorithm Step-by-Step **Prompt:** ``` Explain how the QuickSort algorithm works. Include: - Step-by-step breakdown with a small example array [3, 6, 8, 10, 1, 2, 1]. - Time and space complexity analysis. - A Python implementation with comments. - When to use it vs MergeSort. ``` ### 15. Review Code for Security Issues **Prompt:** ``` Review this Python Flask code for security vulnerabilities. Check for SQL injection, XSS, CSRF, and insecure deserialization. Suggest fixes. @app.route('/user/ ') def profile(username): query = f"SELECT * FROM users WHERE username = '{username}'" result = db.execute(query) return render_template('profile.html', user=result.fetchone()) ``` ## Real-World Case Study: Refactoring a Legacy Codebase **Problem:** A mid-size e-commerce company had a monolithic Django application with 200+ models and 500+ views. Deployment took 45 minutes, and any change required full regression testing. The team wanted to extract the reporting module into a separate FastAPI service. **Solution using GPT-4 prompts:** 1. **Prompt for architecture:** "Given a Django monolith with 200 models and 500 views, suggest a step-by-step plan to extract the reporting module into a FastAPI microservice. Include API gateway considerations, database sharing strategy, and data synchronization." 2. **Prompt for data migration:** "Write a Django management command to export all report data (models: Order, Payment, Refund) to a PostgreSQL database for a new FastAPI service. Use bulk_create and handle incremental updates." 3. **Prompt for API design:** "Design a REST API for a reporting service with endpoints: GET /reports/sales (daily, weekly, monthly), GET /reports/refunds, GET /reports/top-products. Include pagination, date filtering, and caching headers." 4. **Prompt for testing:** "Generate pytest tests for the new FastAPI reporting endpoints. Include integration tests with a test database, and mock external dependencies." **Results:** - The team extracted the module in 3 weeks instead of estimated 8 weeks. - Deployment time dropped from 45 minutes to 5 minutes for the rest of the monolith. - The reporting service scaled independently, handling 10x traffic without affecting the main app. - Code review time decreased because GPT-4 generated consistent, documented code. ## Conclusion These 15 prompts cover the most common developer tasks: generating, debugging, refactoring, explaining, and designing code. The key is specificity—the more context you provide, the better the output. Start with one prompt today. Paste a real function, add your constraints, and see the difference. Over time, you'll build a personal library of prompts that save hours each week. *All examples tested with GPT-4 and GPT-4o in May 2025. Results may vary with model updates.*

15 Prompts for GPT-4: From Code Generation to System Architecture

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