> ## Documentation Index
> Fetch the complete documentation index at: https://docs.figr.design/llms.txt
> Use this file to discover all available pages before exploring further.
# How Global Memory Works
> Understand Figr's persistent memory system that learns and remembers your product context across all projects and sessions.
# How Global Memory Works
Figr's memory system is what makes product-aware design possible. Unlike other AI tools that forget everything between sessions, Figr builds a permanent understanding of your product, team, and users.
**The game changer:** Upload your design system once, explain your constraints once, show your product once. Figr remembers forever and applies this knowledge to every new design.
## What Makes Figr's Memory Different
**Session-based memory:**
* Forgets everything when you close the tool
* Requires re-explaining context every time
* Can't build on previous design decisions
* Generic outputs that ignore your product reality
**Result:** Constant repetition and generic designs
**Persistent product understanding:**
* Remembers context permanently across sessions
* Builds cumulative knowledge over time
* References past decisions and learnings
* Creates increasingly accurate designs
**Result:** Designs that improve and feel more native over time
## Memory Architecture
**What Figr permanently stores about your product:**
```json theme={null}
{
"colorPalette": {
"primary": "#6366f1",
"semantic": {
"success": "#10b981",
"warning": "#f59e0b",
"error": "#ef4444"
}
},
"typography": {
"headings": "Inter, sans-serif",
"body": "Inter, sans-serif",
"scale": "1.25 (Major Third)"
},
"components": {
"button": "rounded corners, 12px padding",
"cards": "subtle shadow, 8px radius",
"forms": "outline style, error states below"
}
}
```
```json theme={null}
{
"primaryUsers": [
{
"type": "marketing_manager",
"painPoints": ["time-consuming reporting", "data silos"],
"goals": ["quick insights", "campaign optimization"],
"techSavviness": "intermediate"
}
],
"workflows": [
{
"name": "campaign_creation",
"steps": ["audience_selection", "content_creation", "scheduling"],
"painPoints": ["audience_overlap_confusion"]
}
]
}
```
```json theme={null}
{
"platform": "web_responsive",
"frameworks": ["React", "TypeScript"],
"browsers": ["Chrome", "Safari", "Firefox"],
"accessibility": "WCAG_2.1_AA",
"performance": {
"maxLoadTime": "2s",
"imageOptimization": true
}
}
```
**What Figr learns from your product usage:**
**Continuous learning sources:**
* Screen sharing sessions that show actual user behavior
* Design feedback and iteration patterns
* A/B test results and performance data
* User research findings and usability studies
* Analytics data about feature usage and conversion
**How Figr remembers design reasoning:**
```
Design Decision: "Use inline validation for forms"
Reasoning: "User research shows 23% better completion rates"
Context: "Applied to signup, checkout, and profile editing"
Results: "Reduced support tickets by 31%"
Future Application: Figr automatically suggests inline
validation for new forms based on this proven success.
```
**Pattern recognition:**
* What design solutions work for your specific users
* Which interaction patterns reduce friction
* How different user types prefer different approaches
* When to break conventions vs. follow them
## How Memory Builds Over Time
**Day 1: Foundation building**
When you first set up Figr:
* Upload design system assets
* Share your screen to show product
* Provide user research and context
* Define technical and business constraints
**Memory strength:** 60% - Good foundation
**Weeks 1-2: Pattern recognition**
As you create designs and provide feedback:
* Figr learns your design preferences
* Understands which suggestions you accept/reject
* Builds patterns from your specific use cases
* Refines understanding of your user needs
**Memory strength:** 80% - Strong context awareness
**Month 1+: Institutional knowledge**
With continued use and team collaboration:
* Comprehensive understanding of your product ecosystem
* Nuanced grasp of edge cases and exceptions
* Predictive insights about user needs
* Anticipation of design requirements
**Memory strength:** 95% - Near-human product understanding
## Memory in Action: Real Examples
**Memory Context:**
* Product: B2B analytics platform
* Users: Marketing managers (non-technical)
* Key insight: Users need quick scanning, not deep analysis
**Month 1 Design:** Standard dashboard with detailed charts
**Month 6 Design:** Scannable cards with key metrics highlighted, drill-down on demand
**Why the difference:** Figr learned that users prefer overview → detail pattern from usage analytics and user feedback
**Memory Context:**
* Product: Fashion e-commerce app
* Users: Mobile-first, visual browsers
* Key insight: Purchase decisions driven by social proof
**Initial Design:** Product page with standard description
**Evolved Design:** Social proof prominent (reviews, photos), purchase path streamlined
**Learning source:** User session recordings showing how customers actually browse and buy
**Memory Context:**
* Product: HR management system
* Users: HR administrators (expert users)
* Key insight: Efficiency over simplicity
**Early Design:** Simplified interface with guided workflows
**Mature Design:** Information-dense layout with keyboard shortcuts and bulk actions
**Memory evolution:** Learned that expert users value speed over hand-holding
## Managing Your Memory
**View and manage what Figr knows:**
**Memory categories:**
* **Design System** (95% complete)
* **User Research** (78% complete)
* **Product Context** (89% complete)
* **Technical Constraints** (67% complete)
* **Brand Guidelines** (92% complete)
**Continuously improve memory:**
* Updated design tokens
* New user research findings
* Fresh analytics data
* Additional product screenshots
* Correct design suggestions
* Explain design decisions
* Share results from implemented designs
* Document edge cases and exceptions
**Team memory synchronization:**
When team members contribute:
* Individual knowledge becomes shared understanding
* Different perspectives enrich product context
* Collective wisdom improves design quality
* Onboarding new team members preserves knowledge
**Pro tip:** Encourage all team members to add their unique insights. A PM's user understanding + designer's interaction expertise + engineer's technical knowledge = comprehensive product memory.
## Memory Security & Privacy
**Your data, your control:** Figr's memory system is designed with enterprise-grade security and complete data ownership.
**You own all memory data:**
* Export complete memory snapshot anytime
* Delete specific memory components
* Transfer memory between accounts
* Maintain control over sensitive information
**Granular privacy settings:**
```
Memory Component: Design System
Sharing: Team only
Memory Component: User Research
Sharing: Designers + PMs only
Memory Component: Technical Constraints
Sharing: Full team access
```
**For organizations with strict requirements:**
* On-premise memory storage
* Custom retention policies
* Audit logs for memory access
* Integration with existing security systems
## Troubleshooting Memory Issues
**Symptoms:**
* Designs don't match your product style
* Missing key constraints or requirements
* Generic outputs despite uploaded context
**Solutions:**
* Check memory dashboard completeness
* Upload additional reference materials
* Provide more detailed product walkthroughs
* Use feedback system to correct misunderstandings
**Symptoms:**
* Inconsistent design suggestions
* Figr seems confused about requirements
* Mixing patterns from different contexts
**Solutions:**
* Review uploaded materials for conflicts
* Clarify which guidelines take priority
* Update outdated context information
* Use memory management to resolve conflicts
***
**Memory mastery unlocked:** You now understand how Figr builds and maintains deep product knowledge that improves every design.
Discover how your assets and context are retained and managed across projects and time.
[Assets & Retention →](/designing/memory/assets-retention)