Stop Debugging Python the Hard Way—Let AI Do It for You

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Ever spent hours staring at a Python error message that made no sense? Debugging is like solving a mystery where the culprit is always you. But what if you had an AI sidekick who could catch those errors faster than you can Google “Why is my code broken?”
In today’s newsletter, we’ll explore how tools like ChatGPT can revolutionize the debugging process, saving you time, frustration, and maybe even your sanity.

But first, let me share some valuable resources I found this week.

Best Content, News & Resources This Week

1. OpenAI’s GPT Function Calling
   Takeaway: If you haven’t experimented with GPT’s function-calling feature, now’s the time. It allows AI to interpret and execute your code better than ever. Great for automating repetitive coding tasks.

2. Stack Overflow Trends for 2025
   Takeaway: More developers are turning to AI for debugging help, and Python remains a top language. Are you keeping up with the tools your peers are using?

3. Tool Spotlight: DeepSource
   Takeaway: An AI-powered code review tool that integrates seamlessly with GitHub. Think of it as ChatGPT’s professional cousin for code quality checks.

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How to Debug Python Code with AI Tools Like ChatGPT

Debugging Python can feel like a never-ending treasure hunt, where each clue is buried under cryptic error messages. Here’s how ChatGPT (and similar AI tools) can make debugging faster, smarter, and a lot less painful:

1. Understand the Error, Fast

Copy-paste your Python error message into ChatGPT. Instead of vague Stack Overflow threads, ChatGPT can break down the problem in plain English.

Example:
Error: TypeError: unsupported operand type(s) for +: 'int' and 'str'
ChatGPT Output: “This error occurs because you’re trying to add an integer to a string. Check where you’re concatenating variables and ensure they’re of the same type.”

2. Suggest Fixes (With Code)

ChatGPT doesn’t just diagnose issues—it can also rewrite code to fix errors. Just share the problematic snippet, and the tool will suggest edits.
Pro Tip: Add a comment explaining what you intended to do. ChatGPT responds better to context.

3. Check Logic Errors

AI tools are great at catching logic bugs that don’t raise errors but still break your program. For example:

for i in range(1, 10):
    if i == 5:
        break
    print(i)

If you ask ChatGPT, it might point out that you’re prematurely stopping the loop without reaching all elements.

4. Optimize and Refactor

Once your code runs, AI can help clean it up. Ask ChatGPT to suggest optimizations, remove redundancy, or convert your code into a more Pythonic format.

5. Real-Life Application: Debugging a Data Pipeline

Let’s say you’re debugging a Python ETL pipeline, and your script fails when loading data into a database. Instead of going line by line, describe the full pipeline to ChatGPT, including libraries and data formats. It can identify bottlenecks, library mismatches, or faulty function calls.

Best Practices When Using AI Tools:

• Chunk Your Code: Don’t dump 500 lines into ChatGPT. Break it into smaller chunks for better context.

• Iterate: If ChatGPT’s suggestion doesn’t work, clarify your goal or tweak the provided code.

• Validate: Always test the AI-generated solutions in a safe environment before deploying them.

AI tools won’t replace your debugging skills, but they can speed up the process by 10x. And let’s face it—when deadlines are tight, every saved minute matters.

Debugging doesn’t have to be painful anymore. Tools like ChatGPT empower data analysts to solve problems faster, learn new techniques, and focus on insights instead of errors.

Need more tips like this? Reply to this email—I’d love to hear your challenges with debugging or AI tools.