Prototyping is quietly becoming the new spec. In a lot of teams, it’s becoming the actual unit of alignment.

The hard part is not speed. Speed is cheap now. What’s not cheap is making a prototype people can trust. You want the team to treat it like a serious alignment artifact, not a throwaway demo. So, the real bar is that it stays coherent after multiple rounds of change.

So how do you build a prototype that doesn't drift?

1) Use GPT as a co-pilot

To build a prototype that’s worth aligning on, you need more than a screen idea. You need clarity on the problem, the domain you’re operating in, who the users are, what they’re trying to achieve, and what constraints you can’t violate. I build them through design artifacts (user archetypes, JTBD, user flows, IA), because they force clarity.

This is where GPT becomes my co-pilot. It’s my thinking partner to create these design artifacts that shape the prototype. Those artifacts feed my PRD and specs, and more importantly, they discipline how the prototype behaves.

2) Instructions to GPT

If GPT is going to be my working memory and thinking partner, I can’t treat it like a blank chat window. I need to set the role it’s playing, the boundaries it should respect, and how it should behave when reality is unclear.

My instruction set usually includes:

- the role

- my workflow

- UX and design principles

- non-negotiables

- how to handle ambiguity

- how to think

- pushback and challenge

- tone and style

3) Handoff prompts (memory blocks)

Even with Projects and “intrinsic memory,” GPT can still feel unreliable especially when a conversation becomes too long, and you have to continue this in another. Because you will always hit context limit, and the thread becomes too long and heavy. Use effective "hand off" prompts to transfer context.

4) Meta-prompting is a super power

Meta-prompting sounds like an extra step, but it buys me stability. ChatGPT handles reasoning and prompt authoring and the prototyping tool (Figma Make, v0, Google AI Studio) handles execution.

5) Creating a PRD, Roadmap, light-weight design system

The key is that these documents are feedable. They can live inside the codebase or the project folder, and the AI tool (Figma Make, v0, Google AI Studio) can reference them whenever it generates UI, components, or flows.

I wrote the full breakdown -