How to Use ChatGPT for Coding

ChatGPT has quickly become a go‑to assistant for developers of all skill levels. Whether you’re stuck on a bug, need a quick prototype, or want to generate boilerplate code, the model can turn a simple prompt into functional Python (or any language) in seconds. In this guide we’ll explore how to harness that power effectively, walk through real‑world examples, and share pro tips that keep you productive without falling into common traps.

Why ChatGPT Is a Game Changer for Coding

First, it’s important to understand what makes ChatGPT special compared to traditional search engines or static documentation. The model can synthesize information from countless sources, adapt to the exact context you provide, and even generate code that follows best practices—something a keyword search can’t guarantee.

Because it works in natural language, you can ask “Why does this recursion fail for large inputs?” instead of digging through stack‑overflow threads. The instant feedback loop dramatically reduces the time spent on repetitive tasks, freeing mental bandwidth for higher‑level design work.

Getting Started: Setting Up the Playground

Before you start typing code, make sure you have a reliable interface. The OpenAI web UI is great for quick experiments, but for deeper integration you’ll want an API key and a lightweight wrapper.

• Sign up at platform.openai.com and generate a secret key.
• Install the official Python client: pip install openai.
• Store the key securely, e.g., in an .env file and load it with python-dotenv.

With the client ready, a single function can send prompts and receive responses. Below is a minimal helper you can drop into any project.

import os, openai, json
from dotenv import load_dotenv

load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

def chatgpt(prompt, model="gpt-4o-mini", temperature=0.2):
    response = openai.ChatCompletion.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        temperature=temperature,
    )
    return response.choices[0].message.content.strip()

Prompt Engineering Basics

ChatGPT’s output quality hinges on how you phrase the request. Think of a prompt as a contract: you specify the goal, constraints, and any required format. The clearer the contract, the less post‑processing you’ll need.

Key Elements of a Good Prompt

• Task definition: “Write a Python function that …”
• Input/Output examples: Provide a sample call and expected result.
• Constraints: Mention performance limits, library restrictions, or style guides.
• Desired format: Ask for a code block, docstring, or inline comments.

For instance, instead of asking “Can you sort a list?”, ask “Write a Python function stable_sort that takes a list of tuples (name, score) and returns it sorted by score descending while preserving original order for equal scores. Include type hints and a docstring.”

Pro tip: Start with a short “system” message that sets the tone, e.g., “You are a senior Python developer who writes clean, PEP‑8 compliant code.” This guides the model to stay consistent throughout a session.

Practical Example 1: Writing a Function from Scratch

Let’s build a small utility that extracts unique email domains from a list of addresses. We’ll ask ChatGPT to generate the code, then we’ll run it and verify the output.

Prompt

"""
Write a Python function called `extract_domains` that:
- Accepts a list of email strings.
- Returns a sorted list of unique domain names (the part after '@').
- Uses list comprehensions and set operations.
- Includes type hints and a docstring with an example.
Provide only the code inside a markdown code block.
"""

Running chatgpt() with that prompt yields:

def extract_domains(emails: list[str]) -> list[str]:
    """
    Extract unique domain names from a list of email addresses.

    Example:
        >>> extract_domains(["alice@example.com", "bob@test.org", "carol@example.com"])
        ['example.com', 'test.org']
    """
    domains = {email.split("@")[1] for email in emails if "@" in email}
    return sorted(domains)

Copy the function into your script, add a quick test, and you’ll see it works immediately.

if __name__ == "__main__":
    sample = ["alice@example.com", "bob@test.org", "carol@example.com"]
    print(extract_domains(sample))
    # Output: ['example.com', 'test.org']

Pro tip: After generating code, ask the model to “Add unit tests using pytest for the function above.” This gives you a ready‑made test suite you can run with pytest -q.

Practical Example 2: Debugging Existing Code

Debugging is where ChatGPT shines because it can read the problematic snippet, understand the error message, and suggest a fix—all in one go.

Scenario

You have a function that should compute the factorial of a number, but it raises a RecursionError for inputs larger than 900. The code looks like this:

def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n-1)

Instead of manually refactoring, you can ask ChatGPT to pinpoint the issue and propose a solution.

Prompt

"""
The following Python function raises RecursionError for n > 900.
Explain why this happens and rewrite the function to avoid recursion depth limits.
Use an iterative approach, keep the same signature, and include a docstring.
Return only the revised code block.
"""

The model returns an iterative version:

def factorial(n: int) -> int:
    """
    Compute the factorial of a non‑negative integer using an iterative loop.

    Args:
        n: A non‑negative integer.

    Returns:
        The factorial of n.

    Raises:
        ValueError: If n is negative.
    """
    if n < 0:
        raise ValueError("Factorial is undefined for negative numbers.")
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

Running the new version with factorial(1000) now returns a huge integer without crashing.

Pro tip: When debugging, include the exact traceback in the prompt. The model can then map the error line to the fix, saving you time hunting through logs.

Advanced Use Cases: Refactoring and Documentation

Beyond one‑off snippets, ChatGPT can act as a collaborative refactoring partner. Suppose you have a legacy script that mixes I/O, business logic, and configuration in a single file. You want to separate concerns without rewriting everything manually.

Prompt for Refactoring

"""
Refactor the following script into three parts:
1. A `config.py` module that holds all constants.
2. A `utils.py` module with helper functions.
3. A `main.py` that imports the above and contains the CLI entry point.
Preserve the original functionality. Provide the content of each file in separate markdown code blocks.
"""

ChatGPT will output three distinct code blocks, each ready to be saved as its own file. You can then run python -m main and verify that behavior remains unchanged.

Documentation generation works similarly. Feed a class or function and ask for a Sphinx‑compatible docstring, complete with parameter descriptions and return types. This can dramatically reduce the time spent on API docs for internal libraries.

Integrating ChatGPT via API in Your Development Workflow

For teams that want to embed AI assistance directly into IDEs, CI pipelines, or custom tools, the OpenAI API offers a straightforward HTTP interface. Below is a minimal Flask endpoint that receives a code snippet and returns a suggested improvement.

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route("/suggest", methods=["POST"])
def suggest():
    data = request.get_json()
    code = data.get("code", "")
    prompt = f"""You are a senior Python engineer. Review the following code and suggest improvements.
Only return a markdown code block with the revised version.

{code}
"""
    suggestion = chatgpt(prompt)
    return jsonify({"suggestion": suggestion})

if __name__ == "__main__":
    app.run(port=5000)

Developers can curl the endpoint from their terminal, or you can build a VS Code extension that calls it behind the scenes. The key is to keep the prompts concise, include the context you need, and handle rate limits gracefully.

Pro Tips and Common Pitfalls

• Validate before you trust: Always run generated code in a sandbox or with unit tests. The model can hallucinate imports or produce syntactically correct but logically flawed snippets.
• Control temperature: Lower values (< 0.3) yield deterministic output, ideal for boilerplate. Raise it for creative brainstorming.
• Chunk large tasks: Instead of asking for an entire project at once, break it into modules. This reduces token usage and improves accuracy.
• Use system messages: Setting a consistent persona (“You are an expert Rust developer…”) keeps style uniform across a session.
• Mind token limits: Long codebases can exceed the model’s context window. Summarize or send only the relevant sections.

Pro tip: Combine ChatGPT with static analysis tools (e.g., flake8, mypy) in your CI. Let the model generate code, then let the linters enforce quality automatically.

Conclusion

ChatGPT transforms the way developers write, debug, and maintain code. By mastering prompt engineering, integrating the API wisely, and applying rigorous validation, you can turn a conversational AI into a reliable co‑pilot. Whether you’re automating repetitive boilerplate, refactoring legacy scripts, or generating documentation on the fly, the model’s versatility pays off in speed and confidence. Embrace the tool, respect its limits, and watch your productivity soar.