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Debug Your AI-Built App in 5 Easy Steps

My AI-Built App Was Broken. Here’s the 5-Step Process I Used to Debug It.

You’ve been there. You’re “vibe coding” with an AI agent. You feed it a brilliant Product Requirements Document (PRD), and in minutes, it generates a beautiful, functional-looking application. You open the admin panel for the – oh, let’s say, lead-gen SaaS – you just dreamed up. It looks pixel-perfect.

Then, you try to actually use it.

You click the “Add New Question” button… and nothing happens.

This is the most common wall that developers and new IT directors hit when using AI coding assistants. Actually, they hit it with humans too, so while this article is meant to be applied to AI Agents, feel free to use these steps on your new junior hires.

The AI stubbed out a beautiful UI and the placeholder framework. But getting it to wire up the actual functionality, connect the database, and handle user input correctly is where most AI projects die.

The natural instinct is to go back to the chat and say, “Hey, that’s wrong. Fix it.”

As we’ve seen time and time again, this is the fastest way to burn tokens and sanity. The AI doesn’t need complaints; it needs direction. It’s a junior dev with the typing speed of JARVIS, and it needs a senior dev (you) to tell it exactly what to do.

I recently walked through this exact scenario, debugging a “dead” button in an AI-generated app. Here is the 5-step strategic process we used to find the bug, direct the AI, and get a truly functional fix.

Step 1: Be the Detective

Before you ever write a prompt, you must gather evidence. Simply telling the AI “the button doesn’t work” is useless. The AI has no context for “work.” You have to provide the evidence of the failure.

In our app, the “Add Question” button was dead. Here was the immediate diagnostic process:

  1. Right-click and Inspect.
  2. Open the Console tab. I clicked the button. Did any errors pop up? In our scenario, no. This is crucial information. It means the bug isn’t a simple JavaScript error.
  3. Open the Network tab. I clicked the button again. Did a new network call appear (a fetch, POST, or XHR request)? Again, no.

Hypothesis: This wasn’t a broken feature. This was a missing feature. The AI had built a “dumb” button, a UI element with no onClick handler, no event listener, and no connection to any backend logic.

Now I had my evidence. I wasn’t going to the AI with a complaint; I was going with a bug report.

Step 2: The “Do Not Make Changes” Probe

This is the single most important rule in all of AI coding. Never let the AI make a change until you are 100% aligned on the problem.

Armed with my evidence from Step 1, I didn’t say, “Fix the button.” I gave the AI my evidence and asked it to form its own conclusion.

My Prompt: “I went into the admin portal and tried to add a question. Nothing happens when I click the button. I checked the browser’s inspect tools. The console shows no errors, and the network tab shows no activity when I click it.

Please look at the code and tell me what the expected behavior is based on what you’ve built. Do not make changes.”

This prompt is strategic. It forces the AI to:

  1. Acknowledge my evidence (no console errors, no network calls).
  2. Review its own code.
  3. Align its “understanding” of the app with my real-world test.

The AI’s response confirmed my hypothesis: “You are correct. There is no onClick handler […] None of the buttons have actual functionality.”

Step 3: Force the AI to Confirm Its Own Plan

Now that we were aligned on the problem, the AI (as it often does) suggested a solution.

AI Response: “…You will need to create a new form component, add click handlers to the buttons, and then implement the logic to save the new questions to the database.”

A rookie Vibe Coder’s mistake is to just say, “Okay, do it.” Don’t. You have to assume the AI is a well-intentioned but forgetful junior dev. What if it already built that form component and just forgot to connect it?

I prompted it again to be absolutely sure.

My Prompt: “Are you sure that form component is not already built? Please check the code to confirm it doesn’t exist. Do not make changes.”

The AI checked and confirmed: “The form component does not exist.”

Now I was ready to let it code. I took the AI’s own suggested plan from its first response and fed it back as a command.

My Prompt: “Okay, you are correct. Please do this:

  1. Create a form component for adding/editing questions.
  2. Add the onClick handlers.
  3. Implement the logic to save new questions to the database.

Do not change anything else about the app.“

Step 4: The Persistence Test, aka, Catching “In-Memory” Lies

The AI ran the build. It came back with a minor error, which we fixed by feeding the console error back to it (another “Do Not Make Changes” probe).

Then, we had a “fix.” I clicked the “Add Question” button. A form popped up! I filled it out, hit save… and the new question appeared in the list. Success?

No.

This is the next great trap: AI agents love in-memory storage. They will often build a feature that “works” by storing data in a local state variable, which is then erased the moment you refresh the page or log out.

Before you move on, you must run the “Persistence Test.”

  1. I added my new question (“What is your budget?”). It appeared in the list.
  2. I refreshed the entire application.
  3. I logged out and logged back into the admin panel.

Was the question still there? Yes.

This confirmed the AI had actually saved the data to the Superbase database I had set up, not just faked it in the UI’s local state. Now the feature was provisionally “done.”

Step 5: The “Loop Breaker”

Sometimes, you get stuck in a “fix-it loop.” The AI tries a fix. It fails. You report the failure. The AI undoes its fix and tries another fix that’s equally wrong. It oscillates, burning tokens and your time.

This happens because the AI has no memory of its past failures and no evidence of its new one.

The “Loop Breaker” technique is for breaking out of this. You have to provide both memory and evidence.

  1. Get the Evidence: Run the failed fix. Open the Console and copy the exact error message.
  2. Provide the Memory: Remind the AI what it just did (using a changelog or just your last prompt).

Then, you hit it with the “Loop Breaker” prompt.

My Prompt: “I tested the fix you just made, and it has failed. Here is the new console log: [paste the full error log here].

You just attempted to [describe the last change, e.g., ‘connect the form to the database’]. Please review this new console log and the code you just modified. Tell me exactly why this new error is happening.

Do not make changes.”

This is a high-level debugging command. You are giving the AI three things: a new error (the console log), the context of what just changed, and a guardrail (“Do Not Make Changes”). This forces it to stop guessing and start analyzing the relationship between its last action and the new failure.

Management 101 Final Message: Stop Arguing, Start Directing

Debugging an AI-coded application isn’t about writing code. It’s about strategic direction. Your job as the senior dev is to manage your AI assistant. That means:

  • Gathering evidence before you write a prompt.
  • Using “Do Not Make Changes” to align on the problem before the solution.
  • Never trusting a fix until you’ve run the “Persistence Test.”
  • Using the “Loop Breaker” prompt to provide memory and evidence when you’re stuck.

Stop arguing with your AI. Start directing it. You’ll save tokens, you’ll save time, and you’ll finally get that app from 80% “stub” to 100% “shipped.”