A friend of mine spent two hours last month chasing a date-parsing bug in a Python service. On a whim she pasted the failing function into ChatGPT along with the stack trace. It found the timezone assumption in about eleven seconds. She had been staring at the same four lines since lunch.
That is the honest shape of ChatGPT for coding. Not a replacement for thinking, more like a fast second pair of eyes that never gets bored. Used carelessly it produces plausible-looking nonsense you will spend the afternoon unpicking. Used deliberately it removes a specific, real chunk of friction from most working days.
What it is actually good at (and it is not what the demos show)
The demo is always “build me a to-do app”. Nobody needs that. The value sits in smaller, duller work.
Boilerplate and glue code
Turning a JSON payload into TypeScript interfaces. Writing the eighth nearly identical Jest test. Renaming a column across a migration and its fixtures. These jobs are boring in a way that makes mistakes likely, and the model handles them about as fast as you can copy and paste. I keep a short list of prompt snippets for exactly this and probably save an hour a week.
Reading unfamiliar code
Drop in a tangled sixty-line function from a repo you inherited and ask what it does, and what would break if the third argument were null. You get a plain-English summary in seconds. It is sometimes wrong, but it hands you a map to check against, which beats scrolling through git blame at eleven at night.
Translating errors
Stack traces from Rust, C++ or anything involving a linker are frequently hostile. ChatGPT is good at rewriting “expected type parameter, found associated type” into something closer to English, and it usually suggests a next step worth trying.
Arguing with your own design
Ask it to attack your plan. “Here is how I intend to cache user sessions. Give me three reasons this falls over at scale.” Even when the objections are generic, writing them down forces you to answer them properly before you write a line of code.
Where it falls apart
Invented APIs
This is the big one. The model will confidently call a method that does not exist, or invent an option flag that looks exactly like the real ones. Version-sensitive ecosystems are the worst offenders, so anything released in the last year or so needs checking against the actual documentation. The broader lesson about trusting output too quickly is covered well in what ChatGPT gets right and where it gets it wrong, and it applies double when the output is code you are about to run.
Losing the thread past a few hundred lines
Long files degrade quality fast. By the time you have pasted 900 lines of a component, the model starts forgetting constraints you set at the top of the conversation. Feed it the forty lines that matter plus the relevant type definitions, not the whole world.
Subtle correctness bugs
A missing await, an off-by-one in a slice, an exception swallowed inside an async loop. Code that runs, looks right, and quietly loses a record once a week. That is more dangerous than code that crashes loudly, and it is the reason the generated output needs a test around it before it goes anywhere near production.
Prompts that produce usable code
Vague requests get vague answers. “Write a function to validate emails” invites a regex that rejects half your users. A few patterns I keep coming back to:
- State the stack and the versions. “Node 20, Express 4, TypeScript 5.4 in strict mode.” Saves a full round of corrections.
- Show the interface, not the whole file. Paste the type definitions and the one failing function.
- Give the failure, not just the code. The exact error text plus what you expected to happen instead.
- Ask for the smallest possible change. “Rewrite only the loop, leave the rest of the file alone” stops it reformatting your entire codebase.
- Demand a test. “Now write a test that fails when the input array is empty.” Free regression coverage while the context is fresh.
One more habit worth building: ask it to walk through its own answer line by line before you accept it. Roughly half the time the explanation reveals the flaw, and you have saved yourself a debugging session.
A workflow that keeps it in its lane
The loop that works for me looks like this. Sketch the approach myself first, because if I cannot describe the shape of the solution I cannot judge the generated version. Ask for one function at a time. Run it. Write or request a test. Then read the diff like it came from a contractor I have never worked with.
That last step is not optional. Treat the model as a junior developer who has read every Stack Overflow answer ever written and shipped nothing. You still review the pull request. If you want a repeatable structure rather than my ad-hoc version, this five-step AI chatbot workflow with real examples maps neatly onto coding tasks.
Guardrails worth setting before your first commit
Three things bite teams more often than bad code. Pasting secrets, for a start. API keys, connection strings and customer data end up in chat history because it is convenient at the time, and cleaning that up afterwards is genuinely painful. Second, licensing: models occasionally reproduce recognizable chunks of open-source code, and dropping GPL-licensed snippets into a commercial codebase is a conversation you do not want with legal. Third, policy. Plenty of companies have a written position on which tools can touch proprietary source, and it is worth ten minutes to find out what yours says.
The cost side is less obvious than it looks, too. A single subscription is cheap. Twenty developers each generating sprawling, half-reviewed code is not, and the expense shows up later as maintenance rather than on an invoice. There is a decent breakdown of that trade-off in where ChatGPT saves hours and where it quietly costs you.
Beyond the chat box
Most serious coding work has already moved past copy-paste. Editor-integrated assistants see your open files and suggest inline. Terminal-based agents will read a failing test, edit three files and re-run the suite on their own. That autonomy is real but uneven, and the failure modes are different from a chat window: an agent that confidently rewrites four files when one needed a one-line fix is a special kind of annoying. If you are weighing up which assistant fits your habits, this comparison of ChatGPT alternatives picked by what you actually need is a sensible starting point, and what agentic AI really does and where it still fails sets expectations honestly.
The part that still belongs to you
Every prompt in this article assumes the same prerequisite: you can look at a function and tell within ten seconds whether it is any good. That judgement is what makes the tooling useful rather than dangerous. It is also the thing nobody outsources.
So the developers getting the most out of ChatGPT for coding are not the ones with the cleverest prompts. They are the ones who understand their codebase well enough to write a precise request, spot the wrong assumption in the answer, and know which twenty lines to paste. Generation got cheap. Reviewing, specifying and debugging did not. If you spend an hour this week sharpening one of those three skills, you will get more out of the tool than another hour of prompt tinkering ever gives you.