Imagine you come from a non-technical background. You build a piece of software through vibe coding and have a lot of fun, so you want to keep going and dig deeper. But as you continue, development starts to feel worse and worse. Once the software goes live and gets real users, even more issues surface. Why does this happen?
This problem may seem to be caused by development skills, but in reality it is due to software loss of control. The fundamental reason is that we overly pursue growth during the development process, while neglecting the foundation of software engineering itself.
I. What Are Software Engineering Fundamentals, and Why Do We Need Them?
When many people first use AI vibe coding to produce a small app that “runs,” they feel everything is basically done and it can be shipped immediately. It can’t. The truly hard part is usually not “getting it written,” but “whether it can keep working after it’s written.”
That is the most basic concern of software engineering: it doesn’t care whether a piece of code looks pretty; it cares whether the whole thing can survive stably over time. To understand this, we can start from the most fundamental aspects.
1.1 Make it run first, then make it run reliably
When a piece of software is first built, everyone expects it to work as intended. In reality, the more valuable and high-impact a product is, the more likely it will run into problems in the real world. So problems surfacing is completely normal.
Software engineering, as a systems discipline, does not aim to “get it right once.” It starts by accepting a reality: software doesn’t end when it’s written; software must keep working through all kinds of surprises.
That’s why “it runs” is only step one. What really matters is: will it crash when something goes wrong, will a change break it, and can someone else taking over still understand it. But simply understanding 'being able to run' is not enough, problems will gradually arise later on.
1.2 Testing is not an optional extra
Many people find testing annoying. The code already runs — why add another layer of checks?
Because “it looks fine today” does not mean it will be fine tomorrow. Software without tests is fragile. You change a button today and another page breaks. You fix one bug and accidentally break a part that used to work.
Without tests, you cannot be sure you didn’t break existing functionality. With tests, you can automatically verify whether what worked before still works now.
Testing also matters because it helps you catch edge cases early. If you want to build high-quality software, you will inevitably face messy real-world situations — tests help you simulate extreme scenarios ahead of time. In addition to stability issues, there is another dimension that is more easily overlooked.
1.3 Security isn’t just for big companies — small tools need it too
Many people think “security” is only for banks and payment systems. It isn’t. As long as your software accepts user input, connects to the internet, or stores data, security issues exist.
For example: will it crash when users input garbage, can someone access data they shouldn’t see, can uploaded files introduce risk? These problems look small in a side project, but once the product is online, they become real trouble. User privacy matters — no one wants to download an app and then have their phone number circulating on second-hand marketplaces. There is one more thing, it won't affect the operation, but it will affect whether you can still 'understand' it.
1.4 Documentation is the best software development tool
Many formally trained engineers dislike writing documentation, and in the AI era this often turns into an especially bad habit. The reason is simple: documentation is the lowest-cost unit for keeping information current and correct. And today, drafting documentation can be delegated to AI — humans mostly review and sync it.
In software development, the most common problem is not “I can’t write it,” but “two weeks later I don’t even remember why I wrote it that way.” Imagine you add a new feature today; a few days later you forget what it does, which modules it touches, what might be impacted if you change it, and where to start if you want to refactor.
Without documentation, all of that lives only in human memory. But if documentation exists, it becomes a source of truth. More importantly, it gives agents something to reference: they can understand the project background quickly, and your development efficiency improves dramatically.
Documentation also lays the groundwork for collaboration. When building complex software as a team, concise documentation helps everyone sync information and align expectations. Work splits become more efficient. After each person finishes their part, the team can again use documentation to share outcomes and align progress — a very high-output loop.
Documentation is one of the best tools in the AI era. Imagine you are a startup CEO and want to review what your AI “employee” delivered today. You can ask the agent to produce a few hundred words describing what was done — and that can roughly represent what used to take human engineers many days. Even if you don’t understand the tech, you can ask the agent to translate industry jargon into plain language and understand the progress in a few hundred words. Information barriers caused by organizational layers get greatly reduced.
In the AI era, these are software engineering fundamentals. They are basic principles like maintainability, robustness, security, high cohesion, and low coupling. These are exactly what determine whether the software you build is high quality or fragile. When these foundations are ignored, they will be further amplified in the AI environment.
II. The Smarter AI Gets, Why It Can Still Get Worse
You might think: “I’m just building a small project — why make it so complicated? And AI is so smart now.” That’s true. And people do observe that stronger models make software development more reliable, for example with models like Opus 4.8. But what does “reliable” actually mean here? It means that during development, the model is more sensitive to known constraints and critical information. If a key piece of logic is involved, it may proactively verify it after implementing a feature to ensure it didn’t break something — while less capable models often don’t show that behavior. Of course, that reliability typically comes at the cost of some speed. Speed and quality can’t always be perfectly balanced; the best approach is to judge based on the project’s complexity and current state. More importantly: once a project grows beyond what one person can control, becomes a multi-person collaborative codebase, or needs to scale seriously, even the smartest AI can’t guarantee the software will be reliable. AI can’t consider everything at once — attention is limited. When you prioritize speed and ignore engineering fundamentals, the problem becomes especially severe and can spiral into disaster. At this point, the problem is not just theoretical, but will directly affect how you continue to develop.
III. If You Still Want to Build Software with AI, Follow a Few Simple Rules
3.1 Accept reality first: don’t change too much at once
Don’t use AI like this: “Optimize this — and also add a feature while you’re at it.” You lose your ability to judge what caused the problem and what introduced the bug. The right approach is: ask AI to do one thing at a time.
3.2 After changes, verify the most important function still works
This isn’t about building a full testing system. It’s about this: after a change, at least manually verify the core flow still works. For example: can you still log in, is the core feature broken, does the data look abnormal? The point isn’t “comprehensive testing.” The point is to prevent the most basic functionality from being quietly destroyed. Apart from the modification method, there is another issue that is easier to overlook.
3.3 Don’t assume AI is right — make it explain
Don’t just say: “Change this for me.” Add one more sentence: “Before you change it, tell me what parts will be affected.” AI often fails in a specific way: it can be locally correct and globally destructive.
3.4 Don’t rely on memory — leave one sentence of explanation
You don’t need a whole documentation system. You only need one sentence per feature: what it does. For example: this saves user data; this handles login logic. There’s only one goal: future you can still understand it.
3.5 Keep a version you can roll back to
You don’t need a full Git course. You only need one concept: every once in a while, save a “known good version.” Because if the software breaks, you must be able to roll back — instead of trying to fix it blindly forever. Putting these rules together actually points to the same core issue.
3.6 Summary: what you really need isn’t tricks, but a method to keep things from falling apart
If we compress the above into one sentence: don’t change too much at once, verify results, keep it understandable, and keep a rollback path. These four things are simple, but they solve the same core problem: so you don’t end up building something you’re afraid to touch. But these methods are only superficial, the deeper problem lies in the way the system itself changes.
IV. You’re Not Building Software — You’re Building a System That Changes
You won't suddenly realize this, but one day you will realize that you have already started using it in this way.When people first use AI to build software, they often have an illusion: as long as the feature runs, the job is done. But the reality is: software is never “finished.” It “stays alive while changing.” Every modification is not just “optimizing it” — it is redefining it. At first you think you control it; later you realize you’re just following the changes. AI doesn’t change this. It only makes the process faster and more obvious. If you can still clearly explain how it works, it’s still software you can maintain. If you can no longer explain how it works, it becomes “the thing you’re afraid to touch.” That’s the boundary.