Generative UI
Interfaces a model composes at runtime from a fixed component vocabulary, instead of a developer hand-coding every screen state in advance.
◆ Playbook
Generative UI is a model choosing and assembling interface components at runtime — picking a chart vs. a table vs. a form, and filling in its props — rather than a developer pre-building one fixed screen per possible state. The model decides *what* to show; the application still owns *how* each piece renders.
Design Principles
- Constrain generation to a fixed, finite component vocabulary — never let the model emit raw HTML/CSS. It should choose and configure pre-built, pre-tested components via structured output (tool calls / JSON), not author markup.
- Schema-validate every generated payload before render. Treat model output as untrusted input: validate against a strict schema and fail closed to a safe default view on any validation error.
- Stream incrementally. Render a skeleton immediately, then hydrate with structure and content as it arrives, rather than blocking on the full response.
- Keep state ownership with the application, not the model. The model selects and configures views; your code still owns data fetching, validation, and any mutation — never let generated UI directly trigger unvalidated actions.
- Solve accessibility once, at the component level. Because generation composes pre-built components, each component only needs to be accessible once — it doesn't need to be re-verified per generation.
- Always keep a deterministic fallback path. Every generatively-composed view should degrade to a static, testable equivalent; the generative path should never be the only path that renders the data.
Recommended Stack
- Vercel AI SDK's streamUI / React Server Components — the most mature generative-UI primitive as of 2026: the model's tool calls stream React components directly to the client.
- Claude (or another tool-use-capable model) with UI components defined as callable tools with strict JSON-schema inputs — the model 'renders' by calling the right tool with the right props.
- Zod (or an equivalent runtime validator) to check every generated prop payload against its schema before a component ever mounts.
- A small, well-documented component library (e.g. shadcn/ui) so the model is choosing from a constrained, known vocabulary rather than an open-ended design space.
Best Use Cases
- Adaptive dashboards that reconfigure based on a natural-language query ("show churn by region this quarter" → the model picks the chart type and filters, not a fixed pre-built dashboard).
- Dynamic form generation, where fields are added or removed based on earlier answers in a conversation rather than a static form schema.
- Chat-driven data exploration, where the model decides whether a table, a chart, or a one-line summary best answers a specific question.
- Adaptive onboarding flows that change shape based on the user's role or prior answers.
Common Pitfalls
- Letting the model generate raw HTML, CSS, or arbitrary JS directly — this is an XSS risk and produces inconsistent, unmaintainable output. Constrain to a component vocabulary instead.
- Skipping runtime validation and trusting model output structurally — a single malformed prop can crash the render tree with no safe fallback.
- No fallback state — a bad or partial generation leaves the user with a blank screen instead of a safe default view.
- Over-generating: treating every pixel as model-decided when most UIs are mostly static chrome plus a smaller generative region. Over-generation hurts consistency, cost, and latency for little benefit.
Tips
- Start with a small, fixed set of 5-8 components before expanding the vocabulary — prove selection quality on a narrow set first.
- Log every generation (input query, chosen component, props) — it's the dataset you'll need to evaluate and improve selection quality over time.
- Cache generated layouts for identical or near-identical queries where the underlying data hasn't changed.
- Ship the skeleton-then-hydrate streaming pattern from day one — retrofitting it later is much harder than building it in.
✦ Prompts
Full library →◈ News
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