Edit Pending
This Use Case has not been finalized.
Deliver·Design System Infrastructure·Agency·Emerging·DEL-102
Product Primitive Documentation
Value hypothesis
Enables AI to generate task-appropriate, adaptive interfaces rather than generic page layouts by providing the domain-level context that component documentation alone cannot supply.
Quality · Innovation
The design system team and domain teams collaboratively define and document domain objects (product primitives) with structured anatomy, lifecycle states and transitions, relationships to other objects, and appearance rules across surfaces — creating a layer of design system context above components that enables AI to compose adaptive, task-appropriate interfaces rather than generic page layouts. The framework has three layers: product primitives (anatomy, states, relationships, signifiers), surfaces (canvas, confirmation, batch, discovery — each with defined anatomy and composition rules), and intent signals (account age, keywords, object counts) that determine which surface an interface should use. Domain teams contribute vocabulary — the specific objects, states, and relationships in their product area; the design system team contributes grammar — how objects compose on surfaces according to intent signals. Documentation is structured for AI consumption with machine-readable anatomy, state transitions, and composition rules.
Risks in application
Constraint Blindness
The cross-team collaboration required to document product primitives (DS team provides grammar, domain teams provide vocabulary) may stall in organizations where domain teams are not resourced or motivated to contribute structured documentation.
Shallow Solutions
Product primitive documentation may encode a model of the domain that is internally consistent but diverges from actual product behavior — the documentation describes how the product should work, while AI consumers treat it as describing how it does work.
Expertise that differentiates
Design System Logic
Recognizing that component-only context is insufficient for AI to compose meaningful interfaces — investing in the domain-object layer that gives AI the reasoning behind composition decisions, not just the inventory of available components.
Information Architecture
Structuring the three-layer framework (primitives, surfaces, intent signals) so that AI can traverse from user intent to surface selection to component composition without requiring explicit human orchestration at each step.
AI Fluency that assures
Performance Discernment
Creates a layer of design system context above components that enables AI to compose adaptive, task-appropriate interfaces rather than generic page layouts.
Recognizing that component-only context is insufficient for AI to compose meaningful interfaces — investing in the domain-object layer that gives AI the reasoning behind composition decisions, not just the inventory of available components.
Transparency Diligence
Enables AI to compose adaptive, task-appropriate interfaces rather than generic page layouts.
Deployment Diligence
Documentation is structured for AI consumption with machine-readable anatomy, state transitions, and composition rules.
Possible Indicators
Composition appropriateness
proportion of AI-generated interfaces that select the correct surface type and composition rules for the given user intent, comparing with-primitives versus component-only context
Adaptive interface capability
whether AI can generate interfaces that respond to intent signals (adjusting surface type based on account age, object count, or keywords) without explicit human direction