Key concepts

This page defines preset AI's core primitives in plain language. Read it once: every other page in the docs assumes this vocabulary.

What preset AI is

preset AI is the design governance layer. It is the infrastructure that exposes a team's design system to AI agents in a form they can read and defer to. It does not just document rules. It stores design intent in a canonical model, then uses that model to power:

• authoring (Studios + Rules)
• validation (Health + audits + drift checks)
• distribution (MCP, CI, docs, integrations)

Core primitives

Design System

A workspace-scoped system containing tokens, presets, patterns, interactions, recipes, and integration context.

Snapshot

A point-in-time state of system artifacts used for comparison, auditing, and rollback-safe operations.

Practical use:

• compare before/after changes
• trace drift over time
• support release and governance review

Token

A semantic design value (for example color-primary, not just #3B82F6).

Why it matters:

• gives human- and AI-readable intent
• enables consistent, centralized updates
• prevents hardcoded values from spreading

Component

A UI primitive or compound element from your codebase or library (Button, Input, Card, etc.).

Preset

A reusable, intent-based configuration for a component.

Example intent:

• primary-action
• destructive-action

Presets convert "how it should be used" into executable config that AI tools and developers can apply consistently.

Pattern

A higher-order rule for composition and behavior across components.

Patterns define when and how pieces should be combined: not just styling details.

Interaction

State and behavior rules (hover, focus, disabled, transitions, modality-specific behavior).

Rule

An enforceable statement of what is allowed or required. Rules drive validation, drift checks, and AI guardrails.

Drift

Deviation between implemented UI and the design system model.

Typical drift types:

• token drift: hardcoded values that bypass the token layer
• preset drift: manual overrides of canonical configurations
• pattern drift: incorrect composition or sequence

Compliance mode

A policy posture controlling strictness of AI and developer workflows.

Common progression:

• Observe: measure
• Assist: recommend
• Guard: strong warnings + constrained generation
• Enforce: hard blocks + explicit approvals

Proposal

A structured change suggestion that is reviewed before becoming canonical.

Typical flow:

1. generate / propose
2. validate
3. approve
4. apply

System intelligence

The combination of canonical model + usage data + policy + MCP tooling that lets preset AI make context-aware recommendations and constraints.

Product surfaces

The app groups the platform into four areas:

• Studios: compose domain-specific assets (tokens, typography, icons, color, content)
• Rules: define cross-domain enforcement artifacts (presets, patterns, interactions, recipes)
• Health: monitor quality and drift
• Distribute: connect tools and deliver system outputs

Creation vs. consumption

preset AI separates:

• creation: defining and evolving system assets
• consumption: using those assets in code, AI tools, CI, and docs

This separation is key to scale: teams can move fast on creation without losing governance, and consumers always read from the canonical layer.