How can I build an MVP with AI?
Use AI to reduce the effort of testing a specific product assumption, not to build every feature in the imagined product. A useful MVP connects one audience, one problem, and one observable behavior. The prototype can be partly manual if that produces a clearer learning signal.
What does the source say?
Google’s problem-framing guidance recommends defining the non-ML goal before selecting a machine-learning solution. That principle helps avoid confusing technical novelty with customer value.
How can you apply this?
Write the assumption you most need to disprove. Sketch the smallest interaction that would expose it and specify what you will observe. Use an assistant to draft or implement only that interaction. Test with a relevant person, record where the experience breaks down, and decide whether to change the problem, approach, or next experiment. Write down what the prototype deliberately excludes, so a promising demonstration is not mistaken for a complete delivery plan.
What should you watch out for?
A working prototype is not evidence of demand or production readiness. Do not expose sensitive data or accept important transactions without the controls those actions require.