What is an AI code generator?

Technical definition

An AI code generator applies a code-trained language model to produce source code from a specification given in natural language. Output scope varies by tool: a single function, a file, a component, a feature spanning several files, or a complete application with its dependencies and configuration.

Generators divide on one property that decides how much verification you have to do yourself: whether the tool executes what it writes. A generator that returns text hands you unverified code. A generator that runs the project surfaces failures before you see the output.

In plain terms

You describe what the code should do. The tool writes it.

What you get back varies. Some tools give you a block of code to paste somewhere. Others give you a running application.

Why it matters

Code generation moved the hard part of software from writing to specifying and checking.

That is a real gain, and it comes with a specific failure mode. Generated code is confident whether or not it is correct. It will reference a library function that does not exist, invent an API parameter, or produce a form that looks complete and saves nothing. None of that announces itself.

So the discipline is: test behavior rather than reading descriptions. Submit the form and check the record. Click through the flow as a real user would. Try the case you know is awkward.

Generated code is also code you now own. It needs the same care as anything else: keys kept out of the browser, permissions that hold, and input validated on the server.