Sarim Nadeem

Posted on Jun 13

Debugging in the Age of AI: 10 Blunders Beginners Make When They Stop Thinking

#debugging #ai #beginners #productivity

There is a specific scene playing out in offices and bedrooms and university dorms right now, thousands of times a day:

A bug appears.
The developer copies the entire error message.
They paste it into ChatGPT / Copilot / Claude / Cursor.
They paste back whatever code the model spits out.
The error goes away.
They move on.

That last step — "the error goes away, they move on" — is where a generation of developers is silently breaking their own careers.

I am not anti-AI. I use these tools every day. They are genuinely incredible at helping you debug when you already know how to debug. The problem is that beginners are skipping the "learn how to debug" part entirely. They are not getting worse at debugging because they are stupid — they are getting worse because AI hides the symptoms of bad debugging just well enough that you never notice you are doing it wrong.

This post is a tour through what real debugging looks like, and the specific blunders I see beginners make in 2026 because the AI is doing their thinking for them.

The one-line version: AI is a power tool you use inside the debugging loop — it is not a replacement for the loop itself. The developers who keep their debugging skills sharp in the AI era will be the ones in highest demand five years from now.

The systematic debugging loop (the thing AI is letting you skip)

Every effective debugging session follows the same six steps. If you only remember one thing from this post, remember this loop:

Reproduce → Isolate → Bisect → Fix → Verify → Prevent

Step	What it actually means
Reproduce	Get the bug to happen on demand. Exact steps, exact inputs, exact environment.
Isolate	Figure out which layer it lives in — frontend, API, database, infra, third-party.
Bisect	Narrow down further. Which commit? Which function? Which line? Which input?
Fix	Address the root cause, not the symptom.
Verify	Confirm the fix works and nothing else broke.
Prevent	Write a regression test. Add monitoring. Update the runbook.

You will notice that "ask the AI" is not a step. That is intentional. AI is a tool you can use inside any of these steps — but it cannot replace the loop itself. The moment you skip the loop, you are not debugging. You are gambling.

Part 1 — The AI-era beginner blunders

Blunder 1: Pasting the error into the chat without reading it first

This is the foundational mistake. Everything else flows from it.

A stack trace is not garbage text to be handed off to a robot. It is a precise, structured description of what your computer was doing when it failed. The error message tells you the what, the line number tells you the where, and the trace tells you the how you got there.

Before you paste anything into an AI, read it yourself. All of it. Ask:

What is the actual exception class? (NullPointerException vs TimeoutException vs PermissionDenied are completely different stories.)
Which line in your code is at the top of the trace? Not the framework's — yours.
What was the input that caused it?
Is this the original error, or is it wrapping a deeper one? (Look for "caused by" / "during handling of the above exception" / "from previous exception.")

Nine times out of ten, just reading the trace carefully gives you the answer. The other one time, you can ask the AI with vastly more context — and you can tell when its answer is wrong.

If you cannot describe in one sentence what the error is telling you, you are not ready to ask anyone — human or AI — to help you fix it.

Blunder 2: Skipping reproduction because "AI already gave me a fix"

This is the one I see most often, and it is the most dangerous.

The developer sees an intermittent bug. They describe it to the AI. The AI suggests a fix. They apply the fix. The bug appears to go away. They ship it.

Three weeks later, the bug comes back. Or worse — it does not come back, but the user-visible symptom changes into something more subtle that nobody catches for six months.

The rule: If you cannot reproduce the bug on demand before applying a fix, you have no way to verify the fix actually fixed it. You just have a fix you cannot disprove.

A bug you cannot reproduce is a bug you have not understood. Common things to check before you reach for any fix:

What were the exact inputs that triggered it?
Does it happen every time, or sometimes? If sometimes — what is different on the times it happens?
Does it happen in dev, staging, or only production? What is different about those environments?
Is it timing-related? Concurrency? Cache state? Specific data?

This is unglamorous work. AI does not help much with it. You actually have to think. That is the point.

Blunder 3: Patching the symptom and calling it a fix

This is the bug that came from the AI's training data and is now infecting codebases everywhere.

You get a TypeError: cannot read property 'name' of undefined. You paste it in. The AI suggests:

// "Fix" suggested by the AI
if (user && user.name) {
  console.log(user.name);
}

The error goes away. You ship it. You feel smart.

But you never asked the actual question: why is user undefined here in the first place? Should it ever be? Is there a bug upstream that fails to populate it? Is there a race condition where the data has not arrived yet? Is this revealing a deeper problem in how your app handles authentication state?

The AI fixed the symptom. The disease is still there. Six weeks later, you find that user is undefined because an API call silently fails 0.5% of the time — and now you have null-checks littered through the entire codebase masking a real reliability bug.

Always ask "why" at least three times before accepting any fix.

Show the "five whys" walkthrough for this exact bug

Why is user undefined here? → Because the API returned 401.
Why did the API return 401? → Because the token expired.
Why did the token expire without being refreshed? → Because our refresh logic only runs on page load, not before each request.
Why does our refresh logic only run on page load? → Because the original implementation assumed sessions would never outlive a page lifetime.
Why did that assumption hold? → It didn't anymore once we added long-lived single-page navigation.

Now you know what to actually fix: the token refresh policy, not a null-check. The null-check would have hidden the real bug forever.

Blunder 4: Trusting confident-but-wrong AI suggestions

Modern AI assistants are extraordinarily good at sounding confident. They will tell you with total certainty that a library has a method that does not exist, that a config option is named one thing when it is actually named something else, or that the cause of your bug is X when it is actually Y.

This is not the AI lying. It is doing exactly what it was trained to do — produce fluent, plausible-sounding text. "Plausible" and "true" are not the same thing.

The trap for beginners: when you do not know the territory well, you cannot tell a hallucinated answer from a real one. They both sound equally authoritative.

Defensive habits that protect you:

After an AI gives you an answer, look up at least one claim independently. Check the docs. Run the code. Read the source.
If the AI says "this is a known issue with library X," search for it. If you cannot find a single GitHub issue, blog post, or Stack Overflow answer about it, it might not be a real issue.
If the fix involves a function or config option you have never seen — read the official documentation for that function before using it, not after.

The 30 seconds you spend verifying saves you hours of chasing a fictional cause.

Blunder 5: Not bisecting when a bug appeared "out of nowhere"

"It was working yesterday."

You hear this constantly. The thing is — code does not change on its own. If something worked yesterday and broke today, something changed. Your job is to find what.

Beginners reach for the AI here. They describe the bug, the AI guesses what is wrong, they spend an hour chasing the guess.

The pros reach for git bisect.

Click for the actual git bisect workflow

git bisect start
git bisect bad                    # current commit is broken
git bisect good <last-known-good> # tag or commit hash that worked
# Git checks out the midpoint. You test it.
git bisect good   # or `git bisect bad` depending on what you found
# Repeat. Git binary-searches for you.
git bisect reset  # when you're done — returns to your original branch

You can even automate it with git bisect run <script> — git will run the script on each midpoint and use the exit code to mark good/bad automatically. In about 10 steps, this finds the exact bad commit across a thousand commits. Deterministic. No guessing.

The same logic applies to data ("which row triggers the bug?"), to configuration ("which config flag is responsible?"), and to code paths ("comment out half the function and see if it still happens"). Binary search is not just an interview question — it is the single highest-leverage debugging technique you can learn, and AI will never recommend it because it does not know your git history.

Blunder 6: Changing multiple things at once

The AI gives you a 40-line code block. You paste it in. The bug goes away.

Now answer me: what fixed it?

You cannot answer. You changed twenty things at once. Maybe one of them fixed the bug and the other nineteen introduced two new ones you have not noticed yet.

The discipline: change one thing at a time. Run the test. See if the behavior changes. Then change the next thing.

When AI gives you a multi-line patch, do not paste it wholesale. Read it. Decide which specific changes address the bug. Apply only those. If the bug is fixed, leave the rest alone. If it is not, you can keep narrowing.

The phrase "I am not sure exactly what fixed it, but it works now" is a confession. It means you do not know if it actually works — you just know that whatever you did changed the symptom you happened to be looking at.

Blunder 7: Not actually reading logs

This one predates AI but the AI era has made it dramatically worse.

Logs contain the story of what your program did. The whole story. The line before the error often tells you why the error happened. The pattern of logs across many requests tells you which ones succeeded and which ones did not. The timestamps tell you about timing.

But reading logs is boring. It is way more fun to paste the error into an AI and have an answer in seconds.

So beginners skip the logs. They debug from the AI's guesses instead of from the evidence their own system is recording. They end up chasing causes that have no support in the actual data.

A debugging session always starts with the data, never with the explanation. When something breaks:

Pull the logs around the failure.
Read the 20 lines before the error, not just the error itself.
Find a successful request from a similar time window. Compare.
Only then, with the data in hand, form a hypothesis.

The AI is great at helping you interpret what you find in logs. It is terrible at substituting for actually looking at them.

Blunder 8: Letting AI write code you cannot explain to yourself

Here is a useful test. After you accept an AI-generated fix, close the chat window. Now explain to yourself, out loud or in writing:

What this code does, line by line.
Why this particular approach was chosen.
What would happen if [some specific edge case] occurred.
How you would write a test for it.

If you cannot answer any of those, you do not understand the code in your own codebase. You are now maintaining software you cannot reason about. The day a bug appears in that code, you will not be able to debug it — you will have to go ask the AI again, and the AI does not remember the last conversation.

This is the most pernicious long-term effect of AI on beginner developers: a slowly accumulating pile of code in their own repos that they themselves do not understand. Every PR is a small loan against future understanding. Eventually the debt comes due.

The code in your repository is your responsibility. The AI is not on call. You are.

Blunder 9: Skipping verification — "AI said it works"

Closely related to the previous point. The AI generates the fix. You apply it. The original error stops appearing. You ship.

You did not:

Run the test suite.
Test edge cases adjacent to the bug.
Check that performance did not regress.
Verify the fix on production-like data, not your tiny dev dataset.
Check that the AI's "fix" did not break something else.

Verification is the part where you act like a skeptical reviewer of your own work. AI cannot do this for you, because AI does not know what "working" means in your business context. Only you do.

A minimum verification checklist for any fix:

[ ] The original repro case no longer fails.
[ ] The full unit test suite passes.
[ ] At least one new test exists that would have caught this bug.
[ ] You manually tested at least one edge case near the bug.
[ ] You ran the change against a realistic data sample, not just your local one-row test.

Blunder 10: Never asking "how do we prevent this class of bug?"

You fixed the bug. The error stopped. You closed the ticket. Onto the next thing.

But you have a one-time opportunity right now that you will never have again — the opportunity to prevent every future bug like this one from happening.

Could a type system have caught this?
Could a lint rule have caught this?
Could a unit test catch it next time?
Could a monitoring alert catch it before users do?
Is the same anti-pattern present elsewhere in the codebase?
Should this go in the team's debugging runbook so the next person who hits it has a shortcut?

This is the "Prevent" step from the loop at the top, and almost nobody does it — beginners least of all. They are exhausted from the bug. They want it gone. They never harvest the learning.

AI cannot do this step for you, because the learning is about your specific codebase and team. Spend ten minutes on it after every bug. Over a year, this single habit will compound into a meaningful gap between you and the developers who skip it.

A few classic anti-patterns that AI has not solved (it just hides them better)

These are not new — they predate AI. But AI has made them harder to notice, because the AI patches the surface symptom while the underlying habit gets worse.

Debugging by staring at code. If you have been reading the same function for 20 minutes, stop. Run it. Print things. Use a debugger. Code that is not running cannot teach you what it does.
Assuming your mental model is correct. The reason a bug feels impossible is almost always because your mental model of the system is wrong. The bug is reality telling you to update your model. Listen to it.
Confirmation bias. Once you have a theory, you start unconsciously looking for evidence that supports it and ignoring evidence that contradicts it. The most useful question in debugging is: "What would prove my current theory wrong?" Then look for that.
Cargo-cult fixes. "I added a try/catch and the error stopped." Did it? Or is the error now being silently swallowed while the underlying problem continues? try/catch without a deliberate plan for what to do in the catch block is a way of muting your system, not fixing it.

How to actually use AI in debugging (the good version)

I am not telling you to never use AI. I use it for debugging every single day. But there is a way to do it that makes you sharper instead of duller. The shape of it is:

Use AI as a rubber duck, not an oracle. Explain the bug to it. The act of explaining often surfaces the answer yourself before the AI even responds.
Use AI to generate hypotheses, not conclusions. "Give me five possible reasons this could be happening" is a great prompt. "Fix this bug" is not.
Use AI to explain unfamiliar code. Got a stack trace from a library you have never used? Ask the AI to explain what the library does and what that error class usually means. Then go verify in the docs.
Use AI to write the regression test, not just the fix. Once you understand the bug, ask the AI to help you write a test that would have caught it. This is the highest-value use of AI in debugging.
Verify everything. Treat AI output the same way you would treat a Stack Overflow answer from 2014 — useful, often right, sometimes confidently wrong, never the final word.

The beginner checklist

Print this out. Tape it to your monitor. Use it on your next bug.

[ ] I read the error message and stack trace before doing anything else.
[ ] I can reproduce the bug on demand.
[ ] I have looked at the logs, not just the error.
[ ] I have a specific hypothesis about the cause, not just a guess.
[ ] I am changing one thing at a time.
[ ] I am fixing the root cause, not patching the symptom.
[ ] I asked "why" at least three times.
[ ] If AI suggested code, I can explain every line of it.
[ ] I verified the fix with the original repro case and the test suite.
[ ] I wrote a regression test for this bug.
[ ] I asked: "How do we prevent this class of bug from happening again?"

Closing thought

There is no shortcut to becoming a good debugger. AI is not making the skill obsolete — it is making it more valuable, because everyone else is unlearning it.

The developers who will be most in demand five years from now are the ones who can do what AI cannot: sit with a hard bug, form a hypothesis, test it against evidence, and reason their way to a real fix. AI will make the routine parts of that work faster. It will not do the work for you.

Use the tools. Use them aggressively. But do not let them do your thinking for you. The thinking is the job.

What is a debugging mistake AI talked you into recently? Drop it in the comments — I would genuinely love to hear how this is playing out on other people's teams.

Happy debugging. 🔍

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