Ref-013: Ubiquitous Language Dictionary Template

Document Type

Reference / Semantic Standard

Purpose

Defines the template and structure for the Ubiquitous Language Dictionary—the “Rosetta Stone” that ensures Semantic Isomorphism between Engineering, Marketing, and Logistics across all SEA™ Forge domains.

1. Strategic Rationale

Semantic Drift is the silent killer of organizations.

When Marketing sells “Instant” but Engineering builds “Asynchronous,” you create technical debt. When Logistics plans “Same-Day” but Operations means “Within 24 Hours,” you create customer disappointment.

The Ubiquitous Language Dictionary forces Isomorphism:

2. Dictionary Structure

2.1 Term Entry Schema

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term:
  token: "CognitiveLeverage"  # The canonical name
  category: "CORE_CONCEPT"    # CORE_CONCEPT | ENTITY | VALUE_OBJECT | ACTION | INVARIANT
  
  definitions:
    engineering:
      description: "The ratio of (Output Token Value) / (Input Human Energy). Measured in reduced cognitive load per unit of insight."
      constraints:
        - "Must be measurable as a ratio"
        - "Denominator cannot be zero"
      example: "leverage_score = output_value / input_energy"
    
    marketing:
      description: "Moving from 'Doing the work' to 'Designing the machine that does the work.' 10x output, 1x input."
      constraints:
        - "Never use 'automate' when 'leverage' is more accurate"
      example: "Our system multiplies your intellectual leverage by 10x."
    
    logistics:
      description: "Operational efficiency gain through structural optimization rather than increased labor."
      constraints:
        - "Efficiency must be sustainable without burnout"
      example: "Route optimization achieving same deliveries with 30% fewer trucks."
  
  isomorphic_bond:
    truth: "Physics. Both sides agree this is a force multiplier, not a labor replacement."
    validation_rule: "IF marketing promises 'X leverage' THEN engineering MUST demonstrate measurable ratio improvement."

2.2 Category Definitions

Category Description Examples
CORE_CONCEPT Foundational ideas that define the domain Leverage, Sovereignty, Isomorphism
ENTITY Things with identity and lifecycle User, Artifact, Case, Spec
VALUE_OBJECT Immutable data without identity Energy, ContextVector, Cost
ACTION Operations that transform state Reify, Compile, Mutate, Validate
INVARIANT Rules that must always be true “No agent writes code”, “Energy ≤ Capacity”

3. Example Dictionary Entries

3.1 Core Concepts

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terms:
  - token: "CognitiveArchitecture"
    category: "CORE_CONCEPT"
    definitions:
      engineering:
        description: "The topology of data flows, context windows, and inference nodes (LLMs) orchestrated as a Directed Acyclic Graph (DAG)."
        constraints:
          - "Must be representable as a graph"
          - "No cycles allowed in execution flow"
      marketing:
        description: "The invisible nervous system that aligns your team's best thinking with infinite machine memory."
        constraints:
          - "Emphasize 'alignment' over 'replacement'"
      logistics:
        description: "The operational backbone that ensures information flows correctly through the organization."
    isomorphic_bond:
      truth: "Structure = Strategy. The code structure is the organizational chart."
      
  - token: "BoltOn"
    category: "CORE_CONCEPT"  
    definitions:
      engineering:
        description: "Ad-hoc API calls or chat interfaces without shared state or structural integration. High entropy."
        constraints:
          - "ANTI-PATTERN: Must be flagged and rejected"
      marketing:
        description: "The 'Chatbot Trap.' Adding noise to your workflow without adding signal."
        constraints:
          - "Position as the ENEMY"
      logistics:
        description: "Point solutions that don't integrate with the broader system."
    isomorphic_bond:
      truth: "Entropy. Unstructured AI increases chaos. We sell Order."

3.2 Entities

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terms:
  - token: "Intention"
    category: "ENTITY"
    definitions:
      engineering:
        description: "The input entity containing user content and context vector."
        constraints:
          - "Must have context_vector attached"
          - "Cannot be processed without state injection"
      marketing:
        description: "What the user truly wants to achieve—the 'desired future state.'"
      logistics:
        description: "The work order or request that initiates a process."
    isomorphic_bond:
      truth: "The spark that triggers reification. Without intention, no artifact."
      
  - token: "Artifact"
    category: "ENTITY"
    definitions:
      engineering:
        description: "The reified output. State = 1. Structured data (JSON/Code/Table)."
        constraints:
          - "Must NOT be conversational text"
          - "Must pass Pydantic validation"
      marketing:
        description: "The tangible deliverable—what you can point to and say 'we created this.'"
      logistics:
        description: "The work product that can be inventoried, tracked, and versioned."
    isomorphic_bond:
      truth: "If it's not structured, it's not an artifact. It's noise."

3.3 Actions

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terms:
  - token: "Reify"
    category: "ACTION"
    definitions:
      engineering:
        description: "Transform probabilistic LLM output into structured, executable data."
        constraints:
          - "Output must match Pydantic schema"
          - "Must consume Thermodynamic Substrate"
      marketing:
        description: "Turning 'Ideas' into 'Assets' instantly. Words becoming actions."
      logistics:
        description: "Converting a work order into a completed deliverable."
    isomorphic_bond:
      truth: "Type Safety. We do not sell 'Chat'; we sell 'Executable Code/Contracts.'"

3.4 Invariants

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terms:
  - token: "NoAgentWritesCode"
    category: "INVARIANT"
    definitions:
      engineering:
        description: "Agents produce DSL/Specs/Manifests. Only Generators produce code."
        constraints:
          - "CI/CD MUST reject PRs with agent modifications to generated directories"
      marketing:
        description: "You remain the Pilot; the AI is the Engine. Ultimate agency resides with the human."
      logistics:
        description: "Standard Operating Procedures are defined by humans; execution is automated."
    isomorphic_bond:
      truth: "Control. Engineering builds the 'Break Glass' button; Marketing sells the 'Safety.'"

4. Validation Rules

4.1 Cross-Domain Consistency Check

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def validate_term_consistency(term: DictionaryTerm) -> ValidationResult:
    """
    Ensure all domain definitions are isomorphically aligned.
    """
    violations = []
    
    # Check that marketing doesn't promise what engineering can't build
    if term.definitions.marketing promises feature X:
        if X not in term.definitions.engineering.capabilities:
            violations.append(f"Marketing promises '{X}' but Engineering doesn't support it")
    
    # Check that logistics constraints align with engineering
    for constraint in term.definitions.logistics.constraints:
        if not is_implementable(constraint, term.definitions.engineering):
            violations.append(f"Logistics constraint '{constraint}' not implementable")
    
    return ValidationResult(valid=len(violations) == 0, violations=violations)

4.2 Semantic Drift Detection

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def detect_semantic_drift(term: str, usage: DocumentUsage) -> DriftReport:
    """
    Detect when a term is used inconsistently with its dictionary definition.
    """
    canonical = dictionary.get(term)
    actual_meaning = nlp.extract_meaning(usage.context)
    
    similarity = semantic_similarity(canonical.isomorphic_bond, actual_meaning)
    
    if similarity < 0.7:
        return DriftReport(
            term=term,
            document=usage.source,
            expected=canonical.isomorphic_bond.truth,
            actual=actual_meaning,
            drift_magnitude=1 - similarity
        )
    
    return None

5. Usage Guidelines

5.1 When Creating New Terms

  1. Define the Engineering constraint FIRST (The Truth)
  2. Derive Marketing language FROM the constraint
  3. Check that Logistics can operationalize it
  4. Write the Isomorphic Bond (the single truth that binds all three)
  5. Add validation rules

5.2 When Resolving Conflicts

If Marketing and Engineering definitions conflict:

  1. Engineering wins (Structure > Content)
  2. Marketing MUST revise to align
  3. Document the resolution in z_conflict_log.md

6. Integration Points

System Usage
Isomorphic Compiler Terms define valid Entities/Aggregates
Axiological Nexus Terms define what “Coherence” means
Marketing Agent Terms constrain what can be promised
Semantic Debt Ledger Term violations create Debt items

References