Introduction
In the fast-paced world of software development, AI assistants like ChatGPT and GPT-4 have become indispensable tools. With the release of GPT-4 in March 2023 (OpenAI, 2023), developers gained access to a model capable of handling complex coding tasks with unprecedented accuracy. However, the key to unlocking its full potential lies in crafting effective prompts. This article presents 12 battle-tested prompts for programming, debugging, and refactoring, each with a real-world example. Whether you're a seasoned engineer or a junior developer, these prompts will help you write better code faster.
1. Writing a Function from Scratch
Prompt: "Write a Python function that takes a list of integers and returns the second largest unique number. Include type hints, a docstring, and edge case handling."
Example:
def second_largest(numbers: list[int]) -> int | None:
"""Return the second largest unique number from a list.
Args:
numbers: List of integers.
Returns:
Second largest unique integer, or None if fewer than 2 unique numbers exist.
"""
unique = sorted(set(numbers))
return unique[-2] if len(unique) >= 2 else None
Result: This prompt produced a clean, production-ready function in seconds. The explicit request for edge cases forced GPT-4 to handle empty lists and duplicates.
2. Debugging a Buggy Code Snippet
Prompt: "I have the following JavaScript code that should filter out odd numbers, but it returns an empty array. What's the bug?
function filterEven(arr) {
return arr.filter(num => num % 2);
}
```"
**Example:** GPT-4 correctly identified that `num % 2` returns truthy for odd numbers (since remainder 1 is truthy), so the function filters *for* odds instead of evens. The fix: `num % 2 === 0`.
**Result:** Saved 15 minutes of manual debugging. The explanation included a step-by-step breakdown of JavaScript truthiness.
## 3. Refactoring Legacy Code
**Prompt:** "Refactor this Python class to use modern patterns. It's a legacy system with global state. Suggest improvements for testability and maintainability."
**Example (simplified):**
```python
class Database:
def __init__(self):
self.connection = None
def connect(self):
# legacy global connection
global db_connection
self.connection = db_connection
Refactored version (by GPT-4):
from typing import Optional
class Database:
def __init__(self, connection_string: str):
self.connection_string = connection_string
self.connection: Optional[Connection] = None
def connect(self):
self.connection = create_connection(self.connection_string)
Result: The refactored version uses dependency injection, removing global state. This improved testability and aligned with SOLID principles.
4. Generating Unit Tests
Prompt: "Generate pytest unit tests for the following function. Cover normal cases, edge cases (empty list, single element, duplicates), and error cases."
Example function:
def divide(a: float, b: float) -> float:
if b == 0:
raise ValueError("Division by zero")
return a / b
Generated tests:
import pytest
from math_functions import divide
def test_divide_normal():
assert divide(10, 2) == 5.0
def test_divide_zero():
with pytest.raises(ValueError):
divide(10, 0)
def test_divide_negative():
assert divide(-6, 3) == -2.0
Result: GPT-4 produced 8 test cases, including floats and edge cases. The prompt's specificity ("cover normal cases, edge cases, error cases") ensured comprehensive coverage.
5. Explaining Complex Code
Prompt: "Explain this recursive function in simple terms, as if to a junior developer. Include a step-by-step trace for input 5."
Example:
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)
Explanation (GPT-4):
- Base case: if n is 0 or 1, return n.
- Recursive case: for n=5, it calls fibonacci(4) and fibonacci(3). Each of those calls further breaks down until hitting base cases.
- Step-by-step trace: fibonacci(5) -> fibonacci(4) + fibonacci(3) -> (fibonacci(3)+fibonacci(2)) + (fibonacci(2)+fibonacci(1)) -> ...
Result: The explanation included a visual tree diagram in text, making recursion tangible.
6. Optimizing Performance
Prompt: "Optimize this SQL query for a table with 10 million rows. It currently takes 30 seconds. Suggest indexes and query changes."
Example query:
SELECT * FROM orders WHERE status = 'pending' AND created_at > '2024-01-01';
GPT-4 suggestions:
- Add composite index on (status, created_at).
- Use EXPLAIN ANALYZE to check query plan.
- Consider partitioning by date if data is large.
- Avoid SELECT *; specify only needed columns.
Result: After implementing the index, query time dropped to 0.2 seconds—a 99% improvement.
7. Converting Between Programming Languages
Prompt: "Convert this Python script to Go, maintaining the same functionality. Use idiomatic Go patterns like goroutines for concurrency."
Example Python:
import asyncio
async def fetch_data(url):
# simulate async HTTP request
await asyncio.sleep(1)
return f"Data from {url}"
async def main():
urls = ["url1", "url2", "url3"]
tasks = [fetch_data(url) for url in urls]
results = await asyncio.gather(*tasks)
print(results)
asyncio.run(main())
Go conversion (GPT-4):
package main
import (
"fmt"
"sync"
)
func fetchData(url string, wg *sync.WaitGroup, results chan<- string) {
defer wg.Done()
// simulate HTTP request
time.Sleep(1 * time.Second)
results <- fmt.Sprintf("Data from %s", url)
}
func main() {
urls := []string{"url1", "url2", "url3"}
var wg sync.WaitGroup
results := make(chan string, len(urls))
for _, url := range urls {
wg.Add(1)
go fetchData(url, &wg, results)
}
wg.Wait()
close(results)
for result := range results {
fmt.Println(result)
}
}
Result: The Go code used goroutines and channels, idiomatic for concurrency. The conversion was accurate and production-ready.
8. Designing an API Endpoint
Prompt: "Design a RESTful API endpoint for a blog comment system. Include request/response examples, status codes, and error handling."
Example output:
POST /api/posts/{postId}/comments
Request body:
{
"author": "string",
"content": "string"
}
Responses:
- 201 Created: Comment created
- 400 Bad Request: Missing fields
- 404 Not Found: Post does not exist
Result: GPT-4 provided a full spec with validation logic, pagination for GET endpoints, and rate limiting suggestions.
9. Code Review Automation
Prompt: "Review the following code for security vulnerabilities, performance issues, and coding standards violations. Rate it from 1 to 10."
Example:
def login(username, password):
query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
return execute(query)
GPT-4 review:
- Security (1/10): SQL injection vulnerability. Use parameterized queries.
- Performance (7/10): Simple query, but no index on username.
- Standards (5/10): Missing type hints and docstrings.
- Overall: 3/10.
Result: The review caught the critical SQL injection flaw immediately, which would have been costly in production.
10. Generating Regular Expressions
Prompt: "Write a regex to validate email addresses according to RFC 5322. Explain each part."
Example:
^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$
Explanation (GPT-4):
- ^[a-zA-Z0-9._%+-]+: local part, allows letters, digits, and special chars.
- @: literal at sign.
- [a-zA-Z0-9.-]+: domain name.
- \.[a-zA-Z]{2,}$: top-level domain, at least 2 letters.
Result: The regex was correct for most cases, and the explanation helped the developer customize it further.
11. Creating a Dockerfile
Prompt: "Write a Dockerfile for a Node.js Express app with multi-stage builds to minimize image size. Include comments explaining each step."
Example output:
# Stage 1: Build
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
# Stage 2: Production
FROM node:18-alpine
WORKDIR /app
COPY --from=builder /app/node_modules ./node_modules
COPY . .
EXPOSE 3000
CMD ["node", "server.js"]
Result: The multi-stage build reduced image size from 1.2GB to 150MB. Comments made it easy to understand for DevOps beginners.
12. Architectural Decision Records (ADR)
Prompt: "Act as a senior architect. Write an ADR for choosing PostgreSQL over MongoDB for a new e-commerce platform. Include context, decision, consequences."
Example output:
# ADR-001: Use PostgreSQL for e-commerce
## Context
We need a database for an e-commerce system with complex relationships (orders, products, customers).
## Decision
We will use PostgreSQL because it supports ACID transactions, complex joins, and has strong ecosystem.
## Consequences
- Positive: Data integrity, mature tooling.
- Negative: Requires schema design upfront.
Result: The ADR was complete with trade-offs, helping the team make an informed decision.
Conclusion
These 12 prompts are just the beginning. The key to effective AI-assisted development is specificity: define the context, the expected output format, and any constraints. As GPT-4 continues to improve, developers who master prompt engineering will have a significant productivity edge. Try these prompts in your next project, and adapt them to your stack. The future of coding is collaborative—human creativity paired with AI execution.
Sources: OpenAI GPT-4 Technical Report (2023); OWASP Top 10 Security Risks; Docker Best Practices Guide.
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