Methodology — How I work with AI
AI can now design and build production software. Whether the result looks and behaves like your product depends on one thing: the context you give it. This is the practice I run at my own AI startup and install in the teams I work with — and it’s how this site itself is built and maintained.
In brief
01Context beats prompting. Encode tokens, voice, and decisions where AI can read them.
02The design system is the source of truth — live data, not documentation.
03Rules live in the codebase, so the hundredth screen matches the first.
04Judgment stays human: review gates, do-not-build lists, taste.
05Proven at Chime Labs, Smokeball, SkillsAware — and on this site.
01
It’s all about context
Everyone talks about design.md files. A markdown file is just a text file — the format was never the point. What matters is what you encode in it: your design tokens and spacing logic, your brand voice, your component decisions and, critically, the reasoning behind them. Markdown happens to be a format both humans and AI read fluently, which makes it the natural vehicle for any kind of context: design principles, engineering constraints, tone of voice, things deliberately not built and why.
Context
What the AI knows. Design tokens, component inventories, brand voice, prior decisions and their rationale. The richer the context, the less the AI invents.
Rules
What it must respect. Hard constraints that live with the work: use these tokens, never these values; extend these components, don’t fork them.
Skills
What it can execute. Codified, repeatable playbooks — analysis, story breakdown, applying the style guide. Written once, run by anyone.
The tools will keep changing. The principle won’t: the quality of AI output is a function of the quality of its context.
02
Design systems the AI can read
An AI-ready style guide isn’t a PDF — it’s data an agent can read. Design tokens define the values; a component library defines the patterns; Storybook documents behaviour; Figma holds design intent, now readable by agents through MCP. Connected, the style guide stops being documentation that drifts out of date and becomes the live contract every generated screen is checked against.
A new generation of tools makes that contract visible before anything ships. Pencil.dev treats designs as files living in your git repository, editable in the IDE, with agents reading and writing them through MCP. Paper renders a free-form canvas in real CSS, so what you design is already the code you’ll ship. I evaluate these hands-on and critically — some earn a permanent place in the workflow, others get walked away from. The evaluation discipline matters as much as the adoption.
03
The real trick: rules in the codebase
Context that lives in a wiki gets ignored. The unlock is putting the rules where the AI actually works — in the repository. Rules files sit beside the code. Tokens are imported, never re-invented. Component usage is constrained by the system itself. Every agent session starts with the same guardrails, so the hundredth AI-built screen matches the first.
That is what makes the practice safe to scale beyond designers: anyone on the team can direct an AI to build a feature with the security and confidence that it will look and behave the way it should. Design stops being a bottleneck and becomes the operating system the whole team builds within.
04
Where the human stays
None of this removes judgment — it concentrates it. Taste, product strategy, what not to build, when the AI’s answer is confidently wrong: that is the work. I keep explicit review gates ahead of anything customer-facing, structured handoffs with acceptance criteria and do-not-build lists, and a simple standard: no AI output ships that I wouldn’t put my name on.
05
Where this is proven
At Chime Labs, my AI startup, we build AI with AI — the style guide is encoded as rules, playbooks run as skills, and AI-augmented engineering gives a pre-seed team the leverage of a much larger one. At Smokeball and SkillsAware, I led teams through the early shift from static Figma mockups to AI-assisted prototyping — practices that have compounded as the tools matured.
And this site is its own demonstration: designed, restyled, and maintained by an AI agent operating under exactly the rules and context described above.
Working on scaling design with AI — or deciding what to trust it with? Talk to me.
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