| name | blueprint-standards |
| description | Defines structural design artifact formats — information architecture, user flows, content model, brand direction, Visual Tokens, and AI interaction model. Use when creating or reviewing design artifacts that precede prototype generation. |
Blueprint Standards
Purpose
Blueprint artifacts define the structural and visual foundation that prototypes and downstream specs build upon.
They answer:
- What pages and flows exist?
- What data lives where?
- What should the product feel like?
- What concrete visual rules should AI tools reuse?
Artifact Overview
| Artifact | Question It Answers | Template |
|---|
| Information Architecture | What pages exist and how are they organized? | references/ia-template.md |
| User Flows | How do users move through the product to achieve goals? | references/flow-template.md |
| Content Model | What data types exist and how do they relate? | references/content-model-template.md |
| Brand Direction | What does the product look and feel like? | references/brand-direction-template.md |
| AI Interaction Model | How do users interact with AI features? | references/ai-interaction-model-template.md |
Relationship to Other Artifacts
docs/product/vision.md ← Strategic intent
docs/product/design-principles.md ← Trade-off resolutions
docs/product/personas/ ← Who uses it
docs/discovery/opportunities/ ← What problems to solve
docs/discovery/hypotheses/ ← What to validate
↓
docs/product/design/ ← Blueprint
↓
docs/discovery/prototypes/ ← Validation artifacts
↓
docs/prd/ ← Delivery handoff
Scope
Blueprint defines: structure, navigation, data relationships, visual direction, Visual Tokens, interaction patterns
Blueprint defers: component APIs, production-ready design systems, pixel-perfect responsive specs
Key Principles
- Opportunity-grounded: Every major page or flow traces to a real opportunity or support need
- Persona-informed: Flows and terminology reflect actual users
- Consistent across prototypes: Shared blueprint context prevents prototype-to-prototype drift
- AI-readable: Visual Tokens and decisions are written in markdown so coding agents can consume them directly
- Reusable downstream: PRDs, UI specs, and implementation can reference the same artifacts
