You've been in this spot. Claude writes the code. You run it. It breaks. You paste the error back. Claude apologizes, rewrites it. Still broken. Different error this time. You do it again. Twenty minutes later you're further from working code than when you started.
That's not an AI problem. That's a loop problem. The model isn't failing you, the feedback structure is. Once I understood that, I stopped losing hours to these spirals and started resolving them in two or three turns.
Here's how I actually do it.
Why the Default Loop Fails
Most people debug with Claude the same way they'd text a friend. Paste error, say "fix this," paste next error, repeat. The issue is that each turn is stateless in practice even if the context window holds the history. Claude doesn't accumulate a theory about what's wrong. It just reacts to whatever you put in front of it last.
So you get local fixes. Patch on patch. The root cause never surfaces because you never asked for a root cause diagnosis. You asked for a fix, and you got one. It just didn't work.
The model is doing exactly what you asked. That's the problem.
Step One: Stop Asking for Fixes First
When I hit a loop, the first thing I do is ban myself from asking for code for one full turn. Instead I ask Claude to diagnose only.
My prompt looks roughly like this:
Don't write any code yet. I need you to read through what we've built, look at this error, and tell me what you think the actual root cause is. Give me your top two or three hypotheses ranked by likelihood. Explain your reasoning for each.
This forces the model out of patch mode and into reasoning mode. The output you get back is usually illuminating. Half the time Claude surfaces something it was quietly working around for the last three attempts but never said out loud.
You also learn whether the model actually understands the system or has been guessing. If the hypotheses are vague or contradictory, that tells you something too. It tells you the context is probably corrupted or incomplete, and you need to reset before going further.
Step Two: Confirm the Hypothesis Before Touching Code
Once I have the hypothesis list, I don't just pick one and ask Claude to fix it. I ask Claude what I should check to confirm which hypothesis is right. Then I go check it myself.
This sounds slow. It's actually the fastest path. Because if you skip this step and Claude guesses wrong, you burn another turn plus your own time testing the result. Confirming first costs you maybe two minutes and eliminates a class of wrong answers entirely.
What am I checking? Depends on the bug. Sometimes it's logging a value I assumed was correct. Sometimes it's reading the actual API docs instead of trusting what Claude remembered about them. Sometimes it's checking whether a package version changed. Claude will tell you exactly what to verify once you ask.
Step Three: Write the Fix Spec, Not the Fix Request
Here's where most people go back to "okay now fix it." That's still too loose.
Instead I write a one-paragraph spec of what the fix should do. Not the implementation, the behavior. What should be true after this is resolved that isn't true now. What should not change. Any constraints on how we solve it.
Example:
The auth token needs to persist across page refreshes without storing it in localStorage. It should survive a hard reload but not a logout event. Don't change the existing logout logic, just fix the persistence layer. The session cookie approach we discussed is probably the right direction but I want you to confirm that before implementing.
This does three things. It locks the scope so Claude doesn't refactor half your app. It documents the intent so you can evaluate whether the code actually solves the right problem. And it gives Claude a definition of done, which dramatically improves the output quality on the first try.
Step Four: Ask Claude to Predict the Test
Before I run anything, I ask Claude to tell me exactly what I should see if the fix is correct. Step by step. What do I do, what should happen, what does success look like.
This is not just a sanity check. It's a trap for bad fixes. If Claude can't describe a clear, testable success state, the fix is probably underspecified. I've caught several half-fixes this way where Claude patched the visible symptom but left the underlying issue intact. When I asked it to describe the test, it described testing the symptom, not the cause. That's a red flag.
It also makes the testing phase faster for me. I'm not fumbling around trying to figure out if it worked. I have a checklist.
Step Five: One Change Per Turn
If the fix involves multiple changes, I make Claude do them one at a time. One change, I test, I report back, then we move to the next one.
This feels tedious and it is. It's also the only way to know which change actually fixed the problem and whether anything broke along the way. When you batch five changes and something breaks, you have no idea which of the five caused it. Now you're debugging the debugging.
For truly trivial changes I'll relax this rule. Renaming a variable and updating one import, fine, do them together. But anything that touches logic, state, or external calls gets its own turn.
When the Whole Session Is Burned
Sometimes you've been in a thread long enough that the context is just cooked. The model has too much conflicting information. You've told it to try three different approaches and it's trying to reconcile all of them at once. Nothing is working and the responses are getting weird.
Just start a new session. This is the hardest thing to convince yourself to do when you've already spent an hour on something. But I've never regretted it. A fresh session with a clean, tight context almost always resolves in two or three turns what a burned session couldn't resolve in fifteen.
Before I start the new session, I write a short brief. Two paragraphs max. What the system does, what the specific bug is, what I've already confirmed about the root cause, and what I need Claude to do. I drop that at the top of the new conversation and nothing else. No history, no old code unless it's directly relevant, no "here's everything we've tried."
Clean slate. Tight brief. Move fast.
The Pattern Underneath All of This
Every step in this process is doing the same thing. It's separating reasoning from generating. Most debugging loops fail because you're asking Claude to reason and generate at the same time, and it shortcuts the reasoning to get to the code faster.
When you force the reasoning to be explicit and separate, the generation quality goes up significantly. You're not smarter than Claude about code. But you're better at knowing when the reasoning is solid before you let it run.
That's the whole job when you're building with AI. Not writing code. Not prompting better. Knowing when the model's reasoning is ready to trust and when it isn't.
Get that right and the debugging loops mostly disappear. The ones that remain resolve fast because you have a process instead of a panic.
