My Real-World Take: How to Use AI for Code Debugging Without Losing Your Mind

The Bug That Almost Broke Me (Again)

Last month, I was wrestling with a Python script that pulls data from a third-party API, transforms it, and shoves it into a PostgreSQL database. Standard stuff, right? Except it wasn’t. For some reason, after about 500 records, the script would just… hang. No error message, no traceback, just a silent, infuriating halt. My logs showed the last successful record, then nothing. It was like the script decided to take an unscheduled coffee break and never came back.

I spent a solid three hours staring at the code, adding print statements, checking network calls, and verifying database connections. Everything looked fine. The API was responding, the database was accepting inserts, and my data transformation logic seemed sound. This is the kind of bug that makes you question your life choices, the kind that eats into your precious development time and makes you wonder if you should just go back to selling artisanal candles.

This is precisely when I turn to AI. Not as a magic bullet, but as a highly efficient rubber duck that actually talks back, often with surprisingly useful insights. My goal isn’t to have it write the fix for me, but to help me pinpoint the *area* of the problem, or suggest a diagnostic approach I hadn’t considered. It’s about accelerating the discovery phase of debugging, which, honestly, is where most of the time gets wasted.

My First Line of Defense: GitHub Copilot Chat

My first move for **how to use AI for code debugging** is always **GitHub Copilot Chat**. It’s right there in VS Code, integrated into my workflow. I don’t have to copy-paste code out of my editor, which saves a ton of context switching. I opened the chat panel and gave it the full context: the Python script, the specific line where I suspected the hang was occurring (or rather, the line *after* the last successful log entry), and the complete lack of error messages.

My prompt was something like this:

“I have a Python script that processes records from an API and inserts them into PostgreSQL. It hangs silently after about 500 records. No error, no traceback. The last successful log entry is after `db_insert(record)`. What are common reasons for a Python script to hang silently in this scenario, especially involving database interactions or API calls? Here’s the relevant code snippet: [pasted ~100 lines of the processing loop and db_insert function].”

Copilot Chat immediately started listing possibilities. It suggested things like connection pool exhaustion, uncommitted transactions, deadlocks, or even a subtle infinite loop in a data transformation step that wasn’t immediately obvious. It also brought up resource limits on the database or the API rate limits, which I’d already checked, but it was a good sanity check.

One suggestion stood out: “Are you closing your database connection or cursor properly within the loop? If you’re opening a new connection or cursor for each record and not closing it, you might hit a resource limit on the database server or run out of file descriptors on the client side.”

Bingo. I was using a connection pool, but my `db_insert` function, in a moment of late-night coding brilliance, was acquiring a new cursor for *each* insert and not explicitly closing it. The pool was managing connections, but the cursors were piling up. After 500 or so, the database server probably just started rejecting new cursor requests, leading to a silent hang as my script waited indefinitely for a resource that wasn’t coming.

I added `cursor.close()` right after the `execute()` call within my `db_insert` function. Reran the script. It processed all 10,000 records without a hitch. That fix took me maybe 10 minutes after Copilot pointed me in the right direction, saving me hours of manual `pdb` stepping and print-statement debugging. This is why I pay for **GitHub Copilot**. At $10/month, it’s a no-brainer for any developer. It pays for itself in saved time within the first week, easily.

When Copilot Needs a Friend: Calling in Claude or ChatGPT

While Copilot Chat is fantastic for in-context help, sometimes you need a broader perspective or a deeper explanation. Or maybe the bug is so obscure that even Copilot’s suggestions feel too generic. That’s when I’ll copy the problem description and relevant code into **Claude** or **ChatGPT**.

I tend to use **Claude 3 Opus** for more complex, nuanced problems, especially when I need it to reason about larger codebases or architectural issues. Its context window is massive, which means I can paste entire files or even multiple related files without worrying about truncation. For this specific hanging bug, if Copilot hadn’t hit the mark, I would have moved to Claude with a similar, but perhaps more detailed, prompt.

My gripe with these larger models, especially the paid tiers, is the cost. **Claude 3 Opus** isn’t cheap if you’re hitting it hard. I’m on the Pro plan for Claude, which is $20/month, but the actual usage costs for Opus can add up if you’re pasting thousands of lines of code daily. For a solo founder, every dollar counts, and while it’s often worth it, I do find myself being more judicious with my prompts to avoid unnecessary token consumption. It’s not a deal-breaker, but it’s a consideration.

I’ve found that **ChatGPT-4** (the paid version) is also excellent, often providing similar quality to Claude for many debugging tasks. The choice between them often comes down to personal preference and which one feels more intuitive for a given problem. I subscribe to both because sometimes one just ‘gets’ the problem better than the other, or offers a slightly different angle that proves useful.

What AI Still Can’t Do (And What It Gets Wrong)

AI isn’t a replacement for understanding your code or your system. It’s a powerful assistant. I’ve seen it hallucinate solutions, suggest deprecated methods, or completely misunderstand the context of a custom library. You can’t just blindly copy-paste its suggestions. You have to critically evaluate every line it gives you, test it, and understand *why* it’s suggesting that particular fix.

For instance, I once asked Copilot to debug a complex React component that was failing to update state correctly. It suggested a `useEffect` dependency array change that, while syntactically correct, would have created an infinite re-render loop in my specific application context. It didn’t understand the broader state management pattern I was using. That’s a concrete gripe: sometimes the AI’s suggestions are technically valid in isolation but disastrous in your specific architecture.

Another limitation is when the bug isn’t in your code at all, but in an external service, a network configuration, or a subtle interaction between multiple microservices. AI can help you rule out your code as the culprit, but it can’t log into your AWS console and check your security group rules. It can’t tell you that the third-party API changed its authentication method last night without warning. For those, you still need your own detective skills and access to your infrastructure.

The real value of AI in debugging isn’t that it fixes everything for you. It’s that it helps you narrow down the problem space dramatically. It acts as a sounding board, a knowledge base, and a pattern matcher all rolled into one. It helps you ask better questions, which is often half the battle in debugging.

Is AI Debugging Worth the Investment?

Absolutely. For a solo founder or any developer, time is your most valuable asset. Spending hours chasing a phantom bug is a productivity killer. Tools like **GitHub Copilot**, **Claude**, and **ChatGPT** don’t eliminate debugging, but they significantly reduce the time spent on the most frustrating parts of it.

Adjacent reading: AI meeting tools coverage.

The free tiers of some models can give you a taste, but for serious work, you’ll want the paid versions. **GitHub Copilot** at $10/month is, as I said, a steal. For the larger models, I’d recommend trying both **Claude 3 Opus** and **ChatGPT-4** for a month to see which one clicks better with your specific coding style and problem types. The $20/month for each might seem like a lot, but if it saves you even a few hours of debugging per month, it’s paid for itself many times over. I’ve found that the investment in these tools directly translates to more features shipped and less time pulling my hair out. That’s a win in my book.