What It Means to Build Software With AI
You have an idea for an app. You describe it to AI, ask it to build something, and receive code. Soon, a screen appears with buttons, text, and something close to what you imagined.
It is an exciting moment. An idea that once felt out of reach now looks possible.
But what have you created so far? Does the app work, or does it only look as though it works? How do you decide what to ask for next?
Understanding that process is the starting point for building software with AI.
From an idea to working software
In this guide, vibe coding means using everyday language to direct AI as it helps you create and change software.
You might ask:
“Create an app where I can write down a task and mark it as finished.”
AI can turn that request into code: instructions that tell a computer what to do. Depending on the tools you use, it may also help put the code into your project, run it, and investigate problems.
This changes how you can take part in building software. You can describe the behaviour you want without knowing how to write every instruction yourself.
However, your description is only the beginning. Building software also involves deciding what should happen, seeing what actually happens, and correcting the differences.
An app turns actions into results
Think about the task app.
A person types “Buy groceries,” presses Add, and sees the task appear in a list. Later, they mark it as finished.
The visible screen is one part of that experience. Behind it, the app needs rules for handling the person’s actions and information.
For example:
- What happens when they press Add?
- Can they add a task without entering any text?
- Does marking a task as finished change its appearance?
- Will the task still be there after they close the app?
These questions describe the app’s behaviour: what it does in response to different actions and situations.
A screen can look complete while some of that behaviour is still missing. The task may appear in the list but disappear when the app closes. A button may look ready but do nothing when pressed.
To understand what you have built, you need to follow the experience through to its result.
Your description leaves decisions to be made
Every request contains gaps.
“Let me add a task” sounds clear, but it does not say whether tasks should be saved, whether empty tasks are allowed, or whether a person can change a task later.
When details are missing, AI may make choices to fill them. Some choices will suit your intention. Others may surprise you.
You do not need to describe every detail before starting. Often, using an early version helps you discover what matters.
Your role is to make important decisions clear as the work develops. When something behaves differently from what you intended, that difference becomes information for the next change.
For example:
“The task appears correctly, but it disappears when I close and reopen the app. Tasks should stay saved until I delete them.”
That request connects an observed problem with an expected result. It gives the next change a clear purpose.
The basic building cycle
AI-assisted development follows a cycle you will return to throughout a project.
1. Describe what should happen
Start with a result you want someone to achieve.
For the task app:
“A person should be able to enter a task and see it in their list.”
This gives the work a direction.
2. Create or change the code
AI helps translate the description into instructions for the computer.
Those instructions may affect the screen, the rules behind it, the information being saved, or several parts together.
At this point, you have a proposed way to produce the result.
3. Run the software
Running the software lets you interact with what has been created.
Code sitting in a file cannot show you the whole experience. You need to see how the app responds when someone uses it.
4. Compare the result with your intention
Enter a task. Press Add. Look at what happens.
Did the task appear? Is its text correct? If saving was part of the request, does it remain after reopening the app?
This comparison tells you whether the change achieved what you wanted.
5. Use what you learned to choose the next change
If the result is wrong, investigate and correct it. If it works, decide which useful part comes next.
Then repeat the cycle.
You will learn more about each part later in this book. For now, the important idea is that development continues through observation and adjustment.
You direct the work
When you build with AI, you take on the role of a builder who directs and evaluates the work.
You decide what the app is for, what matters in its behaviour, and whether the result is suitable for the people who will use it.
AI can help you explore choices and explain unfamiliar ideas. You can ask:
“Explain what happens to a task after I press Add, using simple language.”
Or:
“Which parts of this request are unclear?”
You can also ask AI to help check its work. Those checks are useful, but you still need to understand what was checked and what the result tells you.
For example, a check that confirms the app opens tells you something useful. It does not tell you whether tasks remain saved.
Your understanding can grow with the project. Begin by learning enough to explain the behaviour you are creating and recognise whether it works.
Progress means a useful result works
Receiving a large amount of code can feel like progress. So can adding several new screens.
A more useful question is:
What can a person now do successfully?
In the task app, being able to add a task, find it after reopening the app, and mark it as finished is meaningful progress. It is a small experience that works from beginning to end.
This gives you a practical way to think about building. Each change should help produce a result you can explain and observe.
The idea to carry forward
Building software with AI is a cycle of describing, creating, running, checking, and improving.
AI helps turn your intentions into code. You guide that process by making decisions and comparing the software’s behaviour with the result you want.
The next blog explains why code can look convincing and still be wrong—and how understanding that helps you make better judgments.