Why does AI-generated code not work, and how do I fix it?

AI assistants can write impressive code, but it frequently fails on the first try. This is normal, and with the right approach, fixing it is usually quick.

Common reasons AI code does not work:

1. Missing context. The AI did not see your full project, so it guessed at function names, file paths, database structure or variable names that do not match yours.
2. Version mismatches. The code uses syntax or library functions from a different version than you have installed, or relies on deprecated functions.
3. Invented functions or packages. AI sometimes uses functions or libraries that do not exist, a form of hallucination.
4. Environment differences. Missing dependencies, different operating systems, file permissions or configuration settings.
5. Misunderstood requirements. The code works but does something slightly different from what you wanted.
6. Edge cases. It works for typical input but breaks on empty data, special characters or unexpected formats.

A reliable process to fix it:

Step 1: Run it and get the exact error. Copy the complete error message and stack trace, not just "it doesn't work."

Step 2: Give the AI full feedback. Share the error, the relevant code, what you expected and what happened. Include your language and library versions.

Step 3: Provide context. Share related files, data structures, sample input or configuration, so the AI is not guessing.

Step 4: Ask for an explanation. "Explain why this error happens before fixing it." Understanding the cause prevents repeated mistakes.

Step 5: Test small pieces. Break the code into parts and test each one separately to locate the problem.

Step 6: Verify libraries. Check the official documentation to make sure functions and packages exist and are used correctly.

Step 7: Start fresh if stuck. If the conversation goes in circles with repeated failed fixes, start a new chat with a clean, complete description of the problem and what you have already tried.

To prevent problems from the start:

Specify language and versions in your first request.
Describe inputs, outputs and constraints clearly.
Ask for tests along with code.
Request simple, readable solutions instead of clever ones.
Use coding tools that can see your project, such as editor integrations or coding agents.

Always review AI code for security issues before using it in production, especially code handling user input, passwords, payments or databases. AI speeds up coding greatly, but testing and understanding the code remain your responsibility.