Understanding Prototyping vs Production Code
People conflate the values of learning/discovery with code as a production asset. Generative AI can accelerate both, but they’re fundamentally different activities.
First, rapid prototyping: Someone with a dream and time but no software experience can prototype using code generation tools. This is fantastic — but the generated artifacts aren’t themselves valuable. Value comes from what you learn by interacting with the prototype. Validating it meets a real need and has potential to please users. You’ll reduce uncertainty and wrong paths in the upcoming build. Get it in front of people fast and expect to throw it away.
Now, production code: If the prototype proves valid, you’ll need software that doesn’t crash, can be extended and scaled. Hire qualified, experienced developers to build this. They’ll capture the prototype’s valuable behaviors while ensuring reliability, maintainability, and scalability. They can use generative AI in their development tools to speed this up, but differently. They’ll work at finer levels of detail, driving decisions about functionality, implementation, and architecture. Their output will look much closer to what they produce without AI assistance. This will take an order of magnitude longer than prototyping but it will differ in it’s fundamental construction in ways a non-engineer won’t necessarily grasp in order to ensure it can continue to deliver value over years of service life.
The prototype teaches you what to build. The production code is what you actually ship. Both are essential, but don’t confuse one for the other.
