How to Fix AI Code That Doesn't Work

AI-generated code breaks. It happens to everyone, from beginners to professionals. The good news: it breaks for predictable, fixable reasons. Once you understand why AI code fails and how to communicate errors back to the AI, you'll fix problems in seconds instead of struggling for hours.

Why AI Code Breaks

Four common causes account for most AI code failures. First, missing context: the AI didn't know about your existing code, database schema, or tech stack, so it made assumptions that don't match your project. Second, outdated patterns: the AI sometimes generates code using deprecated APIs or outdated library syntax. Third, incomplete requirements: you didn't mention edge cases, so the AI didn't handle them. Fourth, wrong assumptions: the AI guessed about something ambiguous in your prompt and guessed incorrectly.

The Error Feedback Loop

When code breaks, use this three-step loop: paste the error, explain what happened, and ask for a fix. In practice, that looks like this:

I'm getting this error when I run the code:

[Paste the exact error message here]

This happens when I [describe the action that triggers the error]. The expected behavior is [what should happen instead]. Please fix this.

The exact error message is the most important part. Don't paraphrase it or describe it from memory. Copy and paste the full error text. The AI can often diagnose the problem instantly from the error message alone.

When There's No Error Message

Sometimes code doesn't crash — it just does the wrong thing. In that case, describe the gap between expected and actual behavior:

The code runs without errors, but:
- Expected: When I click 'Submit', the form data is saved and a success message appears.
- Actual: When I click 'Submit', nothing visible happens. The page doesn't change and no message appears.
- I checked the browser console and see no errors there either.

Describing Bugs to AI

The quality of your bug description directly affects how fast the AI fixes it. Include these four things every time: what you did (the action), what happened (the actual result), what should have happened (the expected result), and any additional context (browser, device, data you were using).

Bug: The search feature returns no results even when searching for items I know exist.

Steps: I type 'laptop' in the search box and press Enter.
Actual: The results section shows 'No items found.'
Expected: It should show the 3 laptop items in the inventory.
Context: The items display correctly on the main inventory page, so the data exists. The issue seems to be in the search logic.

When to Iterate vs. Start Over

Iterate when the code is mostly right and just needs fixes — a broken button, a styling issue, a missing validation. Start over when the fundamental approach is wrong — the AI used the wrong library, chose an incompatible architecture, or misunderstood the core requirement.

A good rule: if you've asked the AI to fix the same issue three times and it keeps producing variations of the same broken code, it's time to start over. Say something like:

The previous approach isn't working. Let's start fresh. I need [restate the requirement clearly]. Please use a different approach than before — the [previous approach] kept causing [problem].

Prevention Is Better Than Debugging

The best way to reduce broken code is to give better initial prompts. Mention your tech stack, specify error handling, describe edge cases, and build one feature at a time. Prevention through clear communication will always be faster than debugging after the fact.