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cognitive/docs/guides/ai_documentation_style.md
Daniel Ari Friedman 6caa1a7cb1 Update
2025-02-07 08:16:25 -08:00

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AI Documentation Style Guide

title: AI Documentation Style Guide type: guide status: stable created: 2024-02-06 tags:

Overview

This guide establishes documentation standards optimized for both human readability and machine processing, enabling hyper-intelligent agents to effectively navigate and utilize the knowledge base.

Machine-Readable Structure

Metadata Standards

---
title: Document Title
type: [concept|guide|api|example|template]
status: [draft|stable|deprecated]
created: YYYY-MM-DD
updated: YYYY-MM-DD
complexity: [basic|intermediate|advanced]
processing_priority: [1-5]
semantic_relations:
  - type: prerequisite
    links: [[prerequisite_doc]]
  - type: implements
    links: [[implementation_doc]]
tags:
  - category
  - subcategory
  - specific_topic
---

Semantic Markup

<!-- Semantic section markers for machine parsing -->
#BEGIN_CONCEPT key_concept_name
Core concept definition and explanation
#END_CONCEPT

#BEGIN_IMPLEMENTATION
Implementation details
#END_IMPLEMENTATION

#BEGIN_VALIDATION
Validation criteria
#END_VALIDATION

Knowledge Graph Structure

Relationship Types

  • Hierarchical
    • is_a: Inheritance relationships
    • part_of: Compositional relationships
    • implements: Implementation relationships

Link Annotations

- [[concept]] {type: prerequisite, weight: 0.8}
- [[implementation]] {type: implements, confidence: 0.9}
- [[related_concept]] {type: semantic_similarity, score: 0.85}

Graph Metadata

graph_properties:
  density: 0.7
  centrality: 0.8
  cluster_coefficient: 0.6

Machine Learning Integration

Training Data Markers

# @training_example
def example_function():
    """
    This example demonstrates concept X.
    Training labels: [concept_x, implementation, basic]
    """
    pass

Model References

model_integration:
  embeddings: sentence-transformers/all-mpnet-base-v2
  classifier: cognitive_model_classifier_v1
  validation: validation_model_v1

Performance Metrics

# @performance_metrics
{
    "accuracy": 0.95,
    "latency": "10ms",
    "resource_usage": "150MB"
}

Intelligent Processing Guidelines

1. Semantic Clarity

  • Use precise, unambiguous terminology
  • Maintain consistent concept references
  • Provide explicit relationship definitions

2. Context Preservation

#BEGIN_CONTEXT
- Execution environment: [[runtime_environment]]
- Required capabilities: [[capability_list]]
- Constraints: [[system_constraints]]
#END_CONTEXT

3. Validation Hooks

# @validation_hook
def validate_implementation():
    """
    Validation criteria:
    1. [[requirement_1]]
    2. [[requirement_2]]
    """
    pass

File Organization

Directory Structure

documentation/
├── concepts/           # Foundational knowledge
│   ├── atomic/        # Indivisible concepts
│   └── composite/     # Combined concepts
├── implementations/   # Concrete implementations
│   ├── core/         # Core functionality
│   └── extensions/   # Extended features
└── validations/      # Validation criteria

File Naming

naming_pattern = {
    'concepts': 'concept_{category}_{name}.md',
    'implementations': 'impl_{system}_{component}.md',
    'validations': 'val_{type}_{target}.md'
}

Processing Instructions

1. Priority Levels

processing_priority:
  P1: "Critical path concepts"
  P2: "Core dependencies"
  P3: "Supporting information"
  P4: "Examples and extensions"
  P5: "Additional context"

2. Processing Directives

#PROCESS_MODE: sequential|parallel
#DEPENDENCY_CHECK: strict|flexible
#VALIDATION_LEVEL: basic|complete

3. Resource Management

resource_requirements:
  memory: "4GB"
  processing_time: "30s"
  api_calls: 10

Validation Framework

1. Consistency Checks

# @consistency_check
def verify_documentation():
    """
    Verify:
    1. Link integrity
    2. Semantic consistency
    3. Implementation alignment
    """
    pass

2. Completeness Metrics

completeness_criteria:
  concepts: 0.95
  implementations: 0.90
  validations: 0.85
  cross_references: 0.80

3. Quality Assurance

# @quality_metrics
{
    "clarity_score": 0.9,
    "completeness_score": 0.85,
    "consistency_score": 0.95
}

Integration Examples

1. Knowledge Integration

# Example of knowledge integration
from cognitive_system import KnowledgeGraph

graph = KnowledgeGraph()
graph.add_concept("[[concept_name]]", {
    "relationships": ["[[related_concept]]"],
    "implementations": ["[[implementation]]"],
    "validations": ["[[validation]]"]
})

2. Processing Pipeline

graph TD
    A[Parse Documentation] --> B[Extract Knowledge]
    B --> C[Build Graph]
    C --> D[Validate]
    D --> E[Integrate]

3. Validation Flow

graph LR
    A[Documentation] --> B[Static Analysis]
    B --> C[Semantic Validation]
    C --> D[Integration Testing]
    D --> E[Knowledge Verification]

Related Documentation

References