ChatGPT for Coding: How to Write, Debug, and Review Code with AI

ChatGPT as a Coding Partner

ChatGPT has become one of the most widely used tools in software development. From solo developers to large engineering teams, it's being used to write boilerplate, debug errors, explain unfamiliar code, and speed up code reviews.

The free version handles most common coding tasks. ChatGPT Plus adds Code Interpreter (now called Advanced Data Analysis), which can actually run your code — not just generate it — making it significantly more powerful for data-related work.

Debugging Errors Effectively

Paste your error and code together. Don't just ask "why is my code broken" — give Claude the full context.

Good debugging prompt structure:

Language: Python 3.11
Error message:
KeyError: 'user_id'

Traceback:
File "app.py", line 47, in get_user
  return data['user_id']

My code:
[paste the relevant function]

What I expected: the function returns the user's ID
What actually happened: it crashes on the second request

The more context you provide, the more accurate the diagnosis. ChatGPT will identify the root cause, explain why it happens, and give you a corrected version.

Common things to include:

• The full error message and traceback
• The code around where the error occurs
• What you were trying to do
• What you've already tried

Writing Code from Descriptions

You don't need to know how to code to get useful code from ChatGPT — but you do need to describe what you want clearly.

Weak prompt: "Write a function to process data"

Strong prompt:

Write a JavaScript function called processOrders that:
- Takes an array of order objects, each with: id (number), 
  status (string), total (number), customerId (number)
- Filters out orders where status is "cancelled"
- Groups remaining orders by customerId
- For each customer, calculates their total spend
- Returns an object where keys are customerIds and 
  values are total spend amounts
- Include JSDoc comments

The more specific you are about inputs, outputs, field names, and edge cases, the closer ChatGPT gets to production-ready code on the first try.

Code Explanation and Learning

If you're reading unfamiliar code — a library, a colleague's PR, or inherited legacy code — ChatGPT can explain it at any level of detail.

Explanation prompts:

Explain what this Python decorator does and why 
someone would use it. I understand basic Python 
but I'm not familiar with decorators yet.

[paste code]

Walk through this SQL query line by line.
Explain what each JOIN does and what the final 
result set looks like.

[paste query]

This is a React component. I'm a backend developer 
learning React. Explain the useEffect and useState 
hooks and why they're used here.

[paste component]

Code Review and Refactoring

Use ChatGPT as a first-pass reviewer before asking colleagues.

Code review prompt:

Review this function and check for:
1. Logic errors or off-by-one errors
2. Security vulnerabilities (SQL injection, XSS, etc.)
3. Performance issues
4. Missing error handling
5. Readability improvements

[paste code]

Be specific about what line numbers have issues 
and why each is a problem.

Refactoring prompt:

Refactor this code to:
- Eliminate the nested for loops using a better approach
- Add proper TypeScript types
- Handle the case where the input array is empty
Keep the same behavior, just improve the implementation.

[paste code]

Code Interpreter: Running Code for Real

ChatGPT Plus users get Code Interpreter, which actually executes Python in a sandbox. This is a major upgrade for data tasks.

What you can do with Code Interpreter:

• Upload a CSV and have ChatGPT analyze it, generate charts, and summarize trends
• Run statistical calculations on your actual data
• Debug code by running it and seeing real output
• Convert file formats (Excel to CSV, JSON to CSV, etc.)
• Generate charts and download them as PNG files

Example workflow:

1. Upload your sales data CSV
2. Prompt: "Analyze this data. Show me monthly revenue trends, top 10 products by sales, and flag any months with revenue drops over 15% compared to the previous month."
3. ChatGPT runs the analysis, shows charts, and gives you a written summary

This is dramatically more reliable than asking ChatGPT to analyze data by description alone — it's working with your actual numbers.

Writing Tests

Unit test generation:

Write pytest unit tests for this Python function.
Test cases should include:
- Normal inputs that should succeed
- Edge cases (empty string, zero, None)
- Invalid inputs that should raise exceptions

[paste function]

Mocking and fixtures:

Write Jest tests for this async JavaScript function 
that calls an external API. Mock the API calls using 
Jest's mock functionality. Test: successful response, 
network error, and invalid response format.

[paste function]

Working with Databases

ChatGPT is strong at SQL — both writing queries and explaining them.

SQL generation:

Write a SQL query for PostgreSQL that:
- Joins the orders table with the customers table on customer_id
- Filters for orders placed in the last 30 days
- Groups by customer
- Shows: customer name, order count, total spend
- Orders by total spend descending
- Limits to top 20 customers

Schema design:

Design a PostgreSQL schema for a simple e-commerce app.
I need to store: users, products (with variants), 
orders, and order items.
Include foreign keys, indexes for common queries, 
and constraints. Show the CREATE TABLE statements.

Practical Tips for Better Results

Specify your tech stack: "I'm using Next.js 14 App Router with TypeScript" gives ChatGPT the context to avoid suggesting outdated or incompatible patterns.

Ask for alternatives: "Show me three different ways to implement this and explain the tradeoffs" often reveals approaches you hadn't considered.

Iterate on the output: If the first result isn't quite right, follow up rather than starting over. "That's good but it doesn't handle the case where the array is empty" gets you there faster.

Keep context in one conversation: ChatGPT remembers what you've discussed in the same chat. Stay in one conversation for a coding session rather than starting new chats for each question.

Don't paste credentials: Never paste API keys, passwords, or sensitive environment variables into ChatGPT. Use placeholder values like YOUR_API_KEY instead.

ChatGPT won't make you a developer overnight, but it will make you dramatically faster at every stage of the coding process — from planning to debugging to documentation.