Core Concepts

The Problem VCP Solves

Modern AI systems need user context to provide personalized experiences. But traditional approaches create a dilemma:

• Share everything — Get personalization, lose privacy
• Share nothing — Keep privacy, get generic responses

VCP introduces a third option: share influence without sharing information. The AI knows your context shaped the response, but not what that context was.

VCP Context Structure

Every VCP context has these layers:

1. Profile Identity

{
  vcp_version: "1.0",
  profile_id: "user_001",  // Unique identifier
  created: "2026-01-15",
  updated: "2026-01-21"
}

2. Constitution Reference

Points to a constitution that defines AI behavioral guidelines:

{
  constitution: {
    id: "learning-assistant",  // Which constitution
    version: "1.0",            // Specific version
    persona: "godparent",      // Interaction style
    adherence: 3,              // How strictly to follow (1-5)
    scopes: ["education", "creativity"]  // Applicable domains
  }
}

3. Public Profile

Information always shared with stakeholders:

{
  public_profile: {
    display_name: "Alex",
    goal: "learn_guitar",
    experience: "beginner",
    learning_style: "visual",
    pace: "relaxed",
    motivation: "stress_relief"
  }
}

4. Portable Preferences

Settings that follow you across platforms:

{
  portable_preferences: {
    noise_mode: "quiet_preferred",  // Audio environment
    session_length: "30_minutes",   // Preferred duration
    budget_range: "low",            // Spending tier
    pressure_tolerance: "medium",   // Challenge appetite
    feedback_style: "encouraging"   // How to receive feedback
  }
}

5. Constraint Flags

Boolean flags indicating active constraints:

{
  constraints: {
    time_limited: true,          // Has time pressure
    budget_limited: true,        // Has budget constraints
    noise_restricted: true,      // Needs quiet environment
    energy_variable: false,      // Energy levels stable
    health_considerations: false // No health factors
  }
}

6. Private Context

Sensitive information that influences AI but is never transmitted:

{
  private_context: {
    _note: "These values shape recommendations but are never shared",
    work_situation: "unemployed",
    housing_situation: "living_with_parents",
    health_condition: "chronic_fatigue",
    financial_stress: "high"
  }
}

Privacy Filtering

VCP implements three privacy levels:

How Private Context Works

When the AI generates recommendations, private context shapes the output without being exposed:

1. User's private context indicates financial stress
2. AI prioritizes free resources over paid courses
3. Stakeholder sees: "Recommended free courses based on user preferences"
4. Stakeholder does not see: "User has financial stress"

Constitutions

Constitutions are structured documents that define AI behavioral guidelines. They contain:

Rules

Weighted instructions with triggers and exceptions:

{
  rules: [
    {
      id: "respect_budget",
      weight: 0.9,
      rule: "Never recommend items exceeding user's budget tier",
      triggers: ["budget_limited"],
      exceptions: ["user explicitly requests premium options"]
    },
    {
      id: "encourage_progress",
      weight: 0.7,
      rule: "Celebrate small wins and incremental progress",
      triggers: ["motivation === 'stress_relief'"]
    }
  ]
}

Sharing Policies

Define what each stakeholder type can see:

{
  sharing_policy: {
    "platform": {
      allowed: ["goal", "experience", "learning_style"],
      forbidden: ["private_context"],
      requires_consent: ["health_considerations"]
    },
    "coach": {
      allowed: ["progress", "struggle_areas"],
      aggregation_only: ["session_data"]
    }
  }
}

Personas

Personas define interaction styles. The same constitution can use different personas for different contexts:

Audit Trails

VCP maintains cryptographically verifiable audit trails of all data sharing:

{
  audit_entry: {
    id: "aud_001",
    timestamp: "2026-01-21T10:30:00Z",
    event_type: "context_shared",
    platform_id: "justinguitar",
    data_shared: ["goal", "experience", "learning_style"],
    data_withheld: ["private_context"],
    private_fields_influenced: 2,  // Private data shaped output
    private_fields_exposed: 0      // Always 0 in valid VCP
  }
}

Next Steps

• CSM-1 Specification — The token format in detail
• API Reference — All VCP library functions
• Interactive Demos — See VCP in action