Prototyping Judgment: When to Use AI Builders Instead of Hand-Written Code

How do you go about building prototypes, and how do you decide when to use AI tools instead of traditional coding? Walk through your approach and ground it in an example from your own work. ```hint Start from what the prototype must prove Tie the tool choice to the question the prototype has to answer and to who will see it, then pick an example where that choice clearly changed how fast or how well you learned. ``` ### Clarifying Questions - Should I describe prototypes I build myself, or how I lead a team's prototyping with designers and engineers? - By traditional coding, do you mean engineers building on the production stack, or me writing the code by hand? - Are you interested in prototypes of AI-powered features specifically, or of any product idea? ### What a Strong Answer Covers - A decision rule based on the prototype's purpose, audience, required fidelity and data sensitivity, rather than on tool preference. - A specific example told in STAR form: the tool choice, what was learned and the product decision it led to. - Awareness of where AI-built prototypes mislead or fail: hard-coded happy paths, security and data handling, and demos mistaken for finished products. - How the prototype moves to engineering: what is handed over, what is thrown away, and how engineers are involved early. - Honest reflection on what you would change in your approach next time. ### Follow-up Questions - An executive sees your AI-built prototype and asks to ship it next sprint. How do you respond? - How do you prototype a feature built on a language model, where the same input can produce different outputs? - When have you decided not to prototype at all, and why?