Product Expert Design

Product Expert Design

Guide for designing agent experts that serve end users through adaptive, personalized experiences.

Codebase Experts vs Product Experts

When to Use

• Building product features that learn from user behavior
• Creating per-user expertise files for personalization
• Implementing AI-driven adaptive UX
• Designing recommendation systems with user mental models
• Evaluating whether product experts are appropriate for your use case
• Building progressive personalization with latency considerations

Product Expert Architecture

┌─────────────────────────────────────────────────────┐
│ User Action (view, click, purchase, etc.)           │
└──────────────────────┬──────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────┐
│ Action Tracker                                      │
│ • Capture action type                               │
│ • Record context (time, device, etc.)               │
│ • Queue for expertise update                        │
└──────────────────────┬──────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────┐
│ User Expertise Store (Database)                     │
│ • Per-user JSONB column                             │
│ • Structured preference model                       │
│ • Behavior patterns                                 │
└──────────────────────┬──────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────┐
│ UI Generation Agent                                 │
│ • Load user expertise                               │
│ • Generate personalized UI                          │
│ • Adapt recommendations                             │
└─────────────────────────────────────────────────────┘

User Expertise Schema

{
  "user_id": "uuid",
  "created_at": "timestamp",
  "updated_at": "timestamp",

  "preferences": {
    "categories": ["tech", "sports"],
    "price_range": {"min": 50, "max": 500},
    "brands": ["Apple", "Sony"],
    "style": "minimalist"
  },

  "behavior_patterns": {
    "active_hours": [9, 10, 11, 14, 15, 20, 21],
    "device_preference": "mobile",
    "session_length_avg": 420,
    "purchase_frequency": "monthly"
  },

  "interaction_history": {
    "views": [
      {"item_id": "123", "timestamp": "...", "duration": 45}
    ],
    "cart_adds": [
      {"item_id": "456", "timestamp": "..."}
    ],
    "purchases": [
      {"item_id": "789", "timestamp": "...", "amount": 299}
    ]
  },

  "inferred_interests": {
    "high": ["wireless headphones", "smart home"],
    "medium": ["fitness trackers"],
    "low": ["gaming"]
  },

  "recommendations_context": {
    "last_shown": ["item1", "item2"],
    "clicked": ["item1"],
    "dismissed": ["item3"]
  }
}

Act-Learn-Reuse for Products

ACT: User Takes Action

async function trackUserAction(
  userId: string,
  action: UserAction
): Promise<void> {
  // Record the action
  await db.userActions.create({
    userId,
    actionType: action.type,
    context: action.context,
    timestamp: new Date()
  });

  // Queue expertise update
  await expertiseQueue.add({
    userId,
    action,
    priority: getActionPriority(action.type)
  });
}

LEARN: Update User Expertise

async function updateUserExpertise(
  userId: string,
  action: UserAction
): Promise<void> {
  // Load current expertise
  const expertise = await loadUserExpertise(userId);

  // Update based on action type
  switch (action.type) {
    case 'view':
      updateViewPatterns(expertise, action);
      break;
    case 'cart_add':
      updatePurchaseIntent(expertise, action);
      break;
    case 'purchase':
      updatePreferences(expertise, action);
      break;
  }

  // Recalculate inferred interests
  expertise.inferred_interests = inferInterests(expertise);

  // Save updated expertise
  await saveUserExpertise(userId, expertise);
}

REUSE: Personalized Experience

async function generatePersonalizedUI(
  userId: string
): Promise<UIConfig> {
  // Load user expertise first
  const expertise = await loadUserExpertise(userId);

  // Generate UI based on expertise
  return {
    recommendations: await getRecommendations(expertise),
    layout: selectLayout(expertise.behavior_patterns),
    promotions: filterPromotions(expertise.preferences),
    navigation: prioritizeCategories(expertise.inferred_interests)
  };
}

Latency Considerations

Product experts face latency challenges that codebase experts don't:

The Problem

User Request → Load Expertise → Generate UI → Response
                    ↓
              Agent thinking time (seconds)
                    ↓
              User waiting... (bad UX)

Solutions

1. Pre-computation

// Update expertise async, not on-demand
// Pre-generate UI components during low traffic

2. Progressive Loading

// Show generic UI immediately
// Load personalized elements async
// Swap in when ready

3. Expertise Caching

// Cache hot user expertise in Redis
// Invalidate on significant changes only

4. Tiered Personalization

// Level 1: Instant (cached preferences)
// Level 2: Fast (simple inference)
// Level 3: Deep (full agent, async)

Privacy and Data Handling

Data Minimization

Only store what you need:

// Good: Store patterns, not raw data
{
  "preferred_price_range": {"min": 100, "max": 300},
  "category_affinity": {"tech": 0.8, "fashion": 0.3}
}

// Bad: Store every view with full context
{
  "views": [/* hundreds of detailed entries */]
}

User Control

Provide transparency and control:

interface UserExpertiseControls {
  viewExpertise(): UserExpertise;
  clearExpertise(): void;
  disablePersonalization(): void;
  exportData(): DataExport;
}

Retention Policies

// Decay old data
function decayOldInteractions(expertise: UserExpertise): void {
  const cutoff = daysAgo(90);
  expertise.interaction_history =
    expertise.interaction_history.filter(i => i.timestamp > cutoff);
}

When to Use Product Experts

Implementation Checklist

Database Setup

• User expertise JSONB column
• Action tracking table
• Index on user_id for expertise lookup

Backend Services

• Action tracking endpoint
• Expertise update worker (async)
• Expertise query API
• Cache layer (Redis)

Agent Integration

• Expertise loading prompt
• UI generation prompt
• Recommendation prompt

Frontend

• Progressive loading UI
• Skeleton states while personalizing
• Fallback to generic experience

Privacy

• Data retention policy
• User control dashboard
• Export/delete functionality

Anti-Patterns

Example: E-commerce Product Expert

## User Expertise Structure

preferences:
  price_sensitivity: high|medium|low
  brand_loyalty: [list of preferred brands]
  category_interests: {category: affinity_score}

behavior:
  browse_vs_buy_ratio: 0.15
  cart_abandonment_rate: 0.4
  avg_time_to_purchase: 3 days

purchase_history:
  total_orders: 12
  avg_order_value: 150
  last_purchase: 2025-01-10

## Personalization Actions

1. Show price-sensitive users sale items first
2. Highlight preferred brands in search results
3. Remind high-abandonment users of cart items
4. Suggest reorder for repeat purchases

Related Skills

• agent-expert-creation: Core expert patterns
• expertise-file-design: Schema design principles
• self-improve-prompt-design: Maintaining accuracy

Last Updated: 2025-12-15

Version History

• v1.0.0 (2025-12-26): Initial release

Converted and distributed by TomeVault — claim your Tome and manage your conversions.