AI-generated code keeps crashing on the first run — don't rush to switch tools or blame the model. Most of the time the problem isn't the code itself, but misaligned requirements: the 'login page' you casually described and the 'login page' the AI understood may be two completely different things from start to finish. The fix is simple: first ask the AI to restate the requirements and its assumptions, and only start once you agree; after an error, share the complete error information and verify in small steps.
First, Understand the Three Most Common Roots of Errors
First, the requirements were too vague. When you say 'build an e-commerce site,' the AI can only guess: which tech stack, what the pages look like, where the data comes from — all filled in by its imagination. The vaguer the requirements, the further the code drifts from your expectations, and the more it breaks at runtime.
Second, the environment and dependencies don't match. The AI writes code for the environment it knows best, while the language version, package versions, and operating system on your machine may be completely different. The code itself is fine, but it fails in a different environment — this is especially common on beginners' machines.
Third, the AI invented an API that doesn't exist. Models will confidently call function or parameter names that simply don't exist — that's hallucination. When you see errors like 'method not found,' check the official documentation first to verify whether that API actually exists, and don't waste time on a fake API.
A Three-Step Debugging Method You Can Follow Directly
Step one: ask the AI to restate the requirements and its assumptions first. Before it writes any code, ask: 'How do you plan to do this? State your understanding and assumptions first.' Correcting a wrong assumption on the spot is far easier than reworking a thousand lines of code later.
Step two: share the complete error, not just a code snippet. Send the AI the error stack trace, the command you ran, the language and dependency versions, and the steps to reproduce. Sharing only a code snippet without the error is like asking a doctor to diagnose you from a thermometer reading alone — with incomplete information, even the strongest model can only guess.
Step three: verify in small steps, changing only one thing at a time. Have the AI change one thing per round, explain what changed and why, and move on only after it runs. Rewriting a large block at once makes it impossible to pinpoint which change broke things.
Remember this order: align requirements first, give complete information second, verify in small steps last. An error isn't the end of AI coding — it's the start of you and the AI getting the requirements straight. Once the requirements are clear, most errors disappear.
