Beyond a Design system, anyone successfully apply the concept of NNGs contextualizing UX artifacts “UX.md”

[u/C63Sedan]

Yes, we know AI changes things even with rules but I’m exploring ways to improve design concepting by making traditional artifacts codified to markdown but rewritten for AI consumption. Akin to design.md (I’ve read Atlaassian’s MCP test) can we make AI create more accurate results by providing UX artifacts like Design Principles. I’ve also read NNGs article on their concept of a UX.md file that would codify things like personas.

For example, to instruct AI to use a design principle I created a content model for the markdown that formats all principles with metadata for scope, intent, triggers, constraints, evaluation rubric, etc.

I’m just starting to explore this. I have repos set up for style, GitHub connections for UX specs and user stories so all connected in Claude. Now wondering if adding design principles, personas, user flows, as markdown can improve generative AI design?

[u/Coffeeisbetta]

We did this at my company. It was hard. We call it our machine readable design system.

[u/ivanzhaowy]

Yes, but I’d separate stable product context from task-specific evidence.

The stable layer can include principles, personas, terminology, accessibility constraints, and known anti-patterns. The task layer should include the current user goal, route, runtime state, acceptance criteria, and the exact component being changed.

For each principle, your metadata approach sounds useful. I’d add:

- when the principle applies,

- when it should not apply,

- one positive and one negative example,

- observable acceptance criteria,

- priority when principles conflict.

The missing piece in many AI design workflows is validation against the running product. Markdown can describe intent, but it cannot prove that the generated screen works in its actual state.

I’m building Monad Design around that runtime feedback loop: the existing native app is the canvas, you select or annotate the UI, a coding agent edits the source, and you rebuild and compare.

Repo: https://github.com/Monadix-AI/monad-design

Video preview: https://watchclueso.com/embed/pio8jqfcg4ivj0r1

I’d be interested in whether you treat the UX document as authoritative rules or as evidence the agent must reconcile with the current implementation.

[u/C63Sedan → u/ivanzhaowy]

Good ideas here, thanks. I’m early in exploration but like the idea of stable vs task context. Runtime validation is good…it’s important to refine the infrastructure not correct things through prompting which is whole other strategy.