ChatGPT for Writing Code: A Practical How-To

ChatGPT for writing code is a practical way to speed up development, learn new APIs, and debug faster. In this guide I’ll show clear, repeatable workflows you can use right away. You’ll learn how to craft prompts, set up pair-programming sessions, test and verify generated code, and avoid common pitfalls. Below, each section uses plain steps, examples, and transitions so you can follow along easily.

(OpenAI’s official prompt engineering guidance is a great single reference if you want to dig deeper: Prompt engineering best practices for ChatGPT). OpenAI Help Center

Why use ChatGPT for writing code?

First, ChatGPT can scaffold work quickly. For instance, it can generate boilerplate, suggest algorithms, and draft tests. Second, it helps with learning: you can ask for explanations, simplified versions, or analogies. Third, it improves iteration speed: you can get immediate feedback and quick refactors.

That said, treat ChatGPT as an assistant — not an automatic commit machine. Verify outputs, run tests, and consider security. Industry guidance and hands-on experience show disciplined prompting plus verification yields the best results. OpenAI Help Center+1

Quick workflow: from prompt to running code

1. State the goal up front. Begin with a one-line description: what you want the code to do.
2. Share context. Add language, framework, and runtime constraints (e.g., “Node.js 20, CommonJS, no external packages”).
3. Ask for small, testable outputs. Request a single function or a minimal module rather than a full app.
4. Ask for tests. Request unit tests (e.g., a few Jest tests or pytest cases). Tests help validate behavior.
5. Run and iterate. Run the code locally. If there’s a failure, paste the error and ask for a focused fix.

This iterative pattern (goal → context → small deliverable → tests → iterate) is effective and repeatable. Many skilled developers use it when pair-programming with an AI. blog.rareschool.com+1

Prompt templates (copyable)

Use templates and adapt them. Below are concise examples.

Write a small function

Task: Write a function that converts "snake_case" to "camelCase".
Language: JavaScript (ES2021)
Constraints: No external libs, include 5 unit tests (Jest).
Return: Code block only, with filename comment.

Debugging

I ran this code and got the error: [paste error]. Here is the function: [paste]. Explain the cause in one sentence, then provide a corrected version and one unit test that demonstrates the fix.

Code review

Please review this file for correctness, style, performance, and security. Return a short list of issues and a revised file applying the suggested improvements.

These templates reflect proven prompt engineering patterns: be specific, give constraints, and request tests. OpenAI recommends putting instructions first and separating context clearly. OpenAI Help Center+1

Pair-programming habits that work

• Test-first ping-pong: Ask the model to propose tests, then request code to pass them. This mirrors human pairing.
• Ask for stepwise plans: Request a numbered plan (“Step 1: … Step 2: …”) before code. That keeps the model focused.
• Limit scope per turn: Keep each request narrow to reduce hallucination risk.
• Use “rubric” checks: Ask the model to verify the result against acceptance criteria you provide.

People who do daily AI pair programming recommend disciplined workflows and frequent, small commits. Those workflows increase reliability and keep you in control. DEV Community+1

Testing and validation (non-negotiable)

Always run tests. Additionally:

• Use unit tests and a few integration tests.
• Run static analysis (linters, type checkers).
• Review security: search for unsanitized inputs, unsafe deserialization, or hardcoded secrets.
• Treat generated suggestions as drafts until verified.

Asking ChatGPT to produce tests alongside code is one of the most effective ways to confirm behavior quickly. Many prompt guides and repositories encourage this exact pattern. GitHub+1

Common use cases & a comparison table

Below is a short comparison of common tasks where ChatGPT helps, and when to prefer manual work.

Use case	ChatGPT is great for	When to do manually
Boilerplate scaffolding	Rapid first draft of project structure	When strict architecture rules or compliance are required
Algorithm brainstorming	Try different approaches quickly	For production-critical algorithms requiring formal proofs
Debugging	Explain errors, suggest fixes, write tests	When root cause involves complex system state or race conditions
Documentation	Generate docstrings, READMEs, examples	When docs need domain-specific legal or safety language
Code review	Catch obvious smells, inconsistent naming	For security audits or deep architectural review

This table helps you decide when to rely on the assistant and when to bring in deeper human expertise.

Security, licensing, and safe use

Be careful with sensitive data. Don’t paste secrets, private credentials, or proprietary code unless your platform and policies allow it. Also, check license compatibility: generated code may be influenced by patterns seen during model training; verify license requirements for your project. GitHub Copilot and OpenAI docs both highlight the need for developer oversight. The GitHub Blog+1

Advanced tips

• Use project context: If your tool supports Projects or persistent context, add key files or architecture notes so the assistant can stay consistent across turns. OpenAI’s Projects feature lets you add context to guide responses. OpenAI Help Center
• Define persona & role: Start with “You are a senior backend engineer…” to guide tone and depth.
• Ask for alternatives: Request two or three ways to implement a feature and a short pros/cons list. That supports informed choice.
• Automate repetitive tasks: Use generated scripts to automate dev tasks but review them before running.

Common pitfalls and how to avoid them

• Blind trust: Don’t accept code without tests.
• Too broad prompts: Narrow the request to one function or bug at a time.
• Security oversights: Scan for injection risks and insecure defaults.
• Overfitting to examples: If you repeatedly ask for the same pattern, prompt for novelty or constraints to avoid stale solutions.

Following these guardrails reduces risk and improves productivity.

Example short session (practical)

1. You: “Write a Python function parse_iso_date(s) that returns datetime.date from YYYY-MM-DD. Include 4 pytest cases.”
2. ChatGPT: Returns code + tests.
3. You: Run tests. If one fails, copy the failing test and stack trace, then ask: “Why did test 3 fail?”
4. ChatGPT: Explains, proposes fix, and suggests a regression test.

This tight feedback loop converges rapidly.

Where to go next (resources)

• OpenAI prompt engineering guide for prescriptive tips. OpenAI Help Center
• Example prompt collections and pair-programming notebooks on GitHub. GitHub
• Blogs and postmortems from engineers who pair with AI daily for practical habits. DEV Community+1

Final checklist before merging generated code

• ✅ Unit tests pass locally and in CI
• ✅ Linter and type checks pass
• ✅ Security scan completed (SAST/secret scanner)
• ✅ License and dependency check done
• ✅ Peer review completed

If everything checks out, you can confidently merge.