Most developers use ChatGPT for coding all wrong.
They type a vague request. They get some code back. They paste it into their project. Then they spend the next hour figuring out why it doesn't work.
We've been there. And we've watched plenty of developers make the same mistakes.
The problem isn't ChatGPT. It's how you're using it.
Used correctly, ChatGPT can genuinely speed up your workflow. Used wrong, it's a source of bugs and frustration. The difference comes down to understanding what the tool is actually good at.
Let's fix that.
What ChatGPT Can Do Well
Before diving into any workflow, it is important to define what ChatGPT can and cannot do.
It's genuinely great at:
Explaining unfamiliar code
Writing boilerplate code
Debugging errors given context
Writing unit tests
Refactoring code based on instructions
Code translation across programming languages
It struggles with:
Understanding the context of the entire codebase
Understanding the project-specific conventions
Architectural decision-making
Writing production code that does not need review
Staying up-to-date with library updates
Think of ChatGPT like a smart colleague who's read a lot of code but has never seen your project. They can help with specific problems. But they need context to be useful.
Step 1: Be Specific
Vague prompts get vague results. This is the number one mistake we see.
Bad prompt:
"Write a function to process data."
Great Prompt:
"Create a function in python in which you have to pass a DataFrame as input with three columns such as 'user_id', 'event_type', and 'timestamp'. You must construct the logic for getting frequencies per day of events for each user in dictionary format. Remove all those rows where there are null values. The use of a docstring along with type hints is absolutely mandatory in your code."
In the second prompt, you provide exact specifications to ChatGPT.
The more specific you are, the less time you'll spend fixing the output.
Step 2: Give It Context
ChatGPT doesn't know your project. Tell it.
What should be included:
Programming language and its version: "Using Python 3.11" or "Using Java 21 and Spring Boot 3.2"
Dependences: "Using FastAPI, SQLAlchemy and Pydantic"
Coding conventions: "Type hints, logging instead of print(), error handling"
Limitations of the architecture: "Strategy pattern" or "Functional style"
Design patterns in the source code: "The same as in this code example: [paste code example]"
Including only a small amount of additional details will have a tremendous impact on the quality of the output.
Step 3: Role-based prompting
Rather than prompt ChatGPT to “write the code,” tell ChatGPT who should write the code.
Example:
“Become a senior Python backend developer. Write a program which will parse the Nginx access logs and count the frequency of IPs. The log files can be large up to 10 GB in size; therefore, you have to use generator for reading the file line by line.”
The fact is that when ChatGPT gets assigned a particular role, it adapts its behavior. It doesn't behave like a "senior engineer" in the same way it behaves as a "beginner-friendly instructor."
Step 4: Use ChatGPT for Debugging
This is what ChatGPT is good at providing that you have all the relevant information ready to go.
Information to provide for debugging:
Error message received
The code associated with the error
Expected results
Actual results
Your efforts to date
Example:
"I receive an exception in the following Java method. The exception is a Null Pointer Exception which arises when the user object is null even though I thought I took care of that. The code is: [code]. What am I doing wrong?"
ChatGPT will look through the code, find the problem, and give you a full explanation on how to fix it.
Step 5: Write Tests, Not Only Code
A use of ChatGPT that is not recognized enough is writing tests.
Don't make the mistake of requesting code and then writing tests yourself, but request both.
Example:
"Create a Python function to calculate compound interest. Also create a pytest test suit which covers normal cases, special cases such as zero interest, and the problem of floating point precision."
ChatGPT will write the function as well as a full test suite. Afterward, you will have the opportunity to test it yourself.
Step 6: Verify Everything
Always remember to verify any code written by ChatGPT.
Your checklist:
Review the logic. Does the code actually do what you asked?
Think about the edge cases. What will happen if there is no data or unusual input?
Give it a go. Run the code and see what happens.
Security considerations, please. Particularly if there’s any form of user input or authentication going on.
Consider the outdated patterns. At some point, ChatGPT will have run out of information. Look to your libraries and APIs.
We have found numerous bugs with this checklist. It just takes a few minutes but will save you hours of debugging.
Common Mistakes to Avoid
Trusting code without review. ChatGPT writes code that looks right. Sometimes it isn't. Always review.
Not giving enough context. If you don't tell ChatGPT about your project, it guesses. And it guesses wrong.
Asking for too much at once. Don't ask for an entire application. Ask for one function. Then another. Build incrementally.
Security issues. ChatGPT sometimes does not create secure code. Inspect everything that accepts input from users.
Based on outdated patterns. ChatGPT may recommend outdated techniques. Confirm everything.
The Bottom Line
ChatGPT is a powerful coding assistant. It's not a replacement for developers. But used correctly, it can dramatically speed up your workflow.
Treat it like a junior developer. Give it context. Review its work. Test everything. Iterate when needed.
If you're not using ChatGPT for coding yet, you're missing out. If you're using it wrong, now you know how to fix that.
FAQ Section
Is ChatGPT able to write production-quality code?
Not always. Nevertheless, the generated code should be checked, as it may fail to consider some edge cases and even provide insecure code samples.
What kind of coding assistance can ChatGPT provide to me?
Elaborate on what you need. Explain your project, the language that you will use to develop it, limitations, and task specifications.
Is ChatGPT able to debug my code?
Yes. Paste your code, error message and describe the expected result.
Is ChatGPT good for learning how to code?
Yes. It is very good at explaining, giving examples and answering questions. Use it to learn, not to skip learning.
Can ChatGPT assist with code review?
Of course. One can use ChatGPT for reviewing the code for the presence of errors, security risks, performance, and code readability.
Is ChatGPT aware of all the latest libraries and frameworks?
The knowledge of ChatGPT has an expiry date. It may not be aware of some of the most recent updates. Always cross-check the versions of libraries.
Is ChatGPT able to write test cases?
Certainly, ChatGPT can write test cases such as unit testing and edge case testing.
When shouldn’t you use ChatGPT?
Never use it for important security code without further analysis. Never use it for architectural decisions without human judgment. Don’t blindly trust it.
