10 Prompts for ChatGPT and GPT-4: Programming, Debugging, Refactoring
Introduction
Every developer knows the frustration of staring at a bug for hours or writing boilerplate that feels like a waste of talent. In 2026, GPT-4 has become an indispensable pair-programmer, but only if you know how to talk to it. The difference between a generic response and a production-ready solution often lies in the quality of your prompt.
This article is a collection of battle-tested prompts I use daily in my workflow. No fluff, no theory — just practical, copy-paste-ready prompts that work. Each prompt includes a real usage example and explanation, so you can adapt it to your own projects.
Why Prompt Engineering Matters for Developers
GPT-4 can generate code, debug errors, and suggest architectures, but its output is only as good as your input. A vague prompt like "write a login system" yields a trivial example. A precise prompt with context, constraints, and expected output yields a production-ready snippet. According to OpenAI's own prompt engineering guide (platform.openai.com/docs/guides/prompt-engineering), specifying the task, providing examples, and defining the output format dramatically improve results.
1. Generate Boilerplate Code with Constraints
Prompt:
Write a Python function that fetches data from a REST API with retry logic (exponential backoff, max 3 retries). Use the `requests` library. Include type hints and a docstring. Return a list of dictionaries or raise a custom exception.
Why it works: It specifies the language, libraries, behavior (retry), and output format. GPT-4 will produce a robust function instead of a trivial requests.get().
Example output (abbreviated):
import requests
from typing import List, Dict
import time
class APIError(Exception):
pass
def fetch_data(url: str, max_retries: int = 3) -> List[Dict]:
"""Fetch data from REST API with exponential backoff."""
for attempt in range(max_retries):
try:
response = requests.get(url, timeout=10)
response.raise_for_status()
return response.json()
except requests.RequestException as e:
if attempt == max_retries - 1:
raise APIError(f"Failed after {max_retries} attempts: {e}")
time.sleep(2 ** attempt)
2. Debug an Error Message
Prompt:
I'm getting this error in my Node.js app:
"TypeError: Cannot read properties of undefined (reading 'map')"
Here's the relevant code:
[PASTE CODE]
What's the most likely cause and how do I fix it?
Why it works: You provide the exact error and context. GPT-4 can trace the issue to an undefined variable, missing async handling, or incorrect data structure. It often suggests a fix with a code diff.
3. Refactor Legacy Code
Prompt:
Refactor this JavaScript code to modern ES6+ syntax. Use arrow functions, const/let, template literals, and destructuring. Keep the same logic. Add comments explaining changes.
[PASTE OLD CODE]
Why it works: You specify the target style and ask for explanations. GPT-4 will produce cleaner code and teach you patterns.
4. Write Unit Tests for a Function
Prompt:
Write pytest tests for the following Python function. Cover normal cases, edge cases (empty input, None), and error cases. Use fixtures and parametrize where appropriate.
[PASTE FUNCTION]
Why it works: You specify the testing framework, coverage expectations, and structure. GPT-4 generates tests that match your code.
5. Explain a Complex Algorithm in Simple Terms
Prompt:
Explain the QuickSort algorithm like I'm a junior developer. Include a step-by-step example with a small array, then provide a Python implementation with comments.
Why it works: You set the audience level and request both explanation and code. GPT-4 will tailor the complexity.
6. Generate SQL Queries from Natural Language
Prompt:
Given a PostgreSQL database with tables: users (id, name, email, created_at), orders (id, user_id, total, status). Write a query to find users who placed more than 5 orders in the last 30 days, along with their total spend. Use JOIN and GROUP BY.
Why it works: You provide schema, business logic, and technical constraints. GPT-4 outputs a valid query.
7. Optimize a Slow Code Path
Prompt:
This Python function is slow for large inputs (n=100,000). Identify bottlenecks and suggest optimizations. Explain time complexity before and after.
[PASTE CODE]
Why it works: You give performance context and ask for complexity analysis. GPT-4 can suggest using sets instead of lists, caching, or vectorization.
8. Generate API Endpoint Documentation
Prompt:
Generate OpenAPI 3.0 documentation for this Flask endpoint. Include request/response schemas, error codes, and example curl commands.
[PASTE CODE]
Why it works: You specify the format (OpenAPI) and what to include. GPT-4 produces structured YAML/JSON.
9. Convert Code Between Languages
Prompt:
Convert this Python script to Go. Preserve the logic and error handling. Use idiomatic Go patterns (goroutines for concurrency, errors as return values).
[PASTE PYTHON CODE]
Why it works: You specify target language, style conventions, and concurrency model. GPT-4 handles the translation.
10. Design a Microservice Architecture
Prompt:
I need to design a microservice for user authentication. Requirements: JWT-based, supports OAuth2 login (Google, GitHub), rate limiting, and stores refresh tokens in Redis. Suggest a high-level architecture, list the main components, and provide a code skeleton for the auth service in Python with FastAPI.
Why it works: You give functional requirements, technology stack, and ask for both architecture and code. GPT-4 produces a blueprint.
Real-World Case Study: Debugging a Race Condition
A colleague spent two hours debugging a race condition in a Node.js microservice. He pasted the code into GPT-4 with the prompt: "This code sometimes returns inconsistent data under high load. Find the race condition and fix it." GPT-4 identified that a shared variable was being mutated without locks, and suggested using async mutex. The fix took 5 minutes.
Conclusion
Prompt engineering is not a buzzword — it's a practical skill that saves hours every week. The prompts above work because they provide context, constraints, and explicit expectations. Start with these templates, then customize them for your own projects. The more specific you are, the better the results. And remember: GPT-4 is a tool, not a replacement for understanding. Always review generated code for security and correctness.
Now go build something great.
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