Lack of logs, tests and checks is the main mistake of the beginner
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When there are no logs, tests and checks in the project, it seems that you have made your life easier. In fact, you just deprived yourself of the ability to understand what's going on. As long as it works, it's invisible. Once something breaks down, it becomes almost impossible to figure it out.
As long as everything works, it seems that there are no problems
At the start, things usually look calm. You asked the AI to write the code, it wrote it, you ran it, and you got the result. There are no errors, the screen does not show anything bad, so everything is fine. At this point, the thought of logs or tests seems superfluous. Why add anything else when everything works.
For a person without experience, this is especially logical. Logs, tests, checks look like something from an “adult development” that is still a long way off. I want to do something workable first, not make my life difficult.
First failure and complete loss of orientation
Problems don't start right away. At some point, you change one little thing, the AI rewrites a piece of code, or a little bit of other data comes in. The result suddenly becomes strange. The program either doesn’t work or doesn’t behave as expected.
And then the unpleasant thing turns out. You don't know exactly where it went wrong. You don't see the intermediate steps, you don't understand what data was inside, and you can't tell at what point everything broke. There is only a feeling of “something is not working”, without leads.
Why don't you see anything without your lairs
Logs are not a complicated engineering thing. These are just traces of what's going on inside the program. They show what data came in, what was done with it, and where it led.
When there are no logs, all you have to do is guess. You can go through the options, ask the AI again and again to “fix” something, but you don’t understand what is being repaired. When you have logs, at least you see the real picture, even if you don't fully understand the code.
For a beginner, this is especially important. Logs allow you to see the process, not just the outcome, and gradually begin to navigate what is happening at all.
Testing is not for ideal, but for reality
AI almost always writes code under ideal conditions. It assumes that the data is correct, the values exist and the format matches expectations. As long as it is, everything looks fine.
The problem is that reality is rarely perfect. As soon as something unexpected comes along, code without validation begins to behave unpredictably. Sometimes he falls, sometimes silently does something strange. And again, you're left without understanding because the code wasn't ready for bad scenarios.
Checks are not for beauty or for “correctness.” They want the program to be clear, even when something goes wrong.
Without tests, it is impossible to know what is normal
Tests often seem superfluous, especially in small projects. It’s easier to just run the code and see if it works. The problem is that the launch only shows one particular case.
Tests record expectations. They answer the question of what should work and how. Without them, you check everything again and by eye. At least you have a point of reference with them.
When working with AI, this is especially important. When the AI changes the code, the tests immediately show what is broken. Without tests, you may not notice the problem for a long time or not understand where it came from.
Why this stage is almost always missed
Beginners don’t miss logs, tests, and checks because they’re lazy. Because they don't make sense until the first major failure. While everything is working, it seems that this is an extra load.
But it is the absence of these things that makes any failure painful. You don’t understand what happened, and you start working blindly, hoping that the AI will guess the right solution.
What changes when there is at least one
As soon as simple logs, basic checks and at least a few tests appear in the project, the work changes very noticeably. Mistakes cease to be a mystery. You start to see where and why things go wrong.
AI is also starting to help better. When you can show the logs or tell which test fell, the conversation becomes concrete. Instead of the abstract “doesn’t work,” there is a clear point from which to work.