# AI Documentation Style Guide --- title: AI Documentation Style Guide type: guide status: stable created: 2024-03-15 tags: - ai - documentation - style - guidelines semantic_relations: - type: implements links: [[documentation_standards]] - type: relates links: - [[ai_semantic_processing]] - [[ai_validation_framework]] --- ## Overview This guide establishes standards for AI-augmented documentation in the cognitive modeling framework, ensuring consistent and effective use of AI tools for documentation enhancement. ## Machine-Readable Structure ### Metadata Standards ```yaml --- 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 ```markdown <!-- 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 ```markdown - [[concept]] {type: prerequisite, weight: 0.8} - [[implementation]] {type: implements, confidence: 0.9} - [[related_concept]] {type: semantic_similarity, score: 0.85} ``` ### Graph Metadata ```yaml graph_properties: density: 0.7 centrality: 0.8 cluster_coefficient: 0.6 ``` ## Machine Learning Integration ### Training Data Markers ```python # @training_example def example_function(): """ This example demonstrates concept X. Training labels: [concept_x, implementation, basic] """ pass ``` ### Model References ```yaml model_integration: embeddings: sentence-transformers/all-mpnet-base-v2 classifier: cognitive_model_classifier_v1 validation: validation_model_v1 ``` ### Performance Metrics ```python # @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 ```markdown #BEGIN_CONTEXT - Execution environment: [[runtime_environment]] - Required capabilities: [[capability_list]] - Constraints: [[system_constraints]] #END_CONTEXT ``` ### 3. Validation Hooks ```python # @validation_hook def validate_implementation(): """ Validation criteria: 1. [[requirement_1]] 2. [[requirement_2]] """ pass ``` ## File Organization ### Directory Structure ```text documentation/ ├── concepts/ # Foundational knowledge │ ├── atomic/ # Indivisible concepts │ └── composite/ # Combined concepts ├── implementations/ # Concrete implementations │ ├── core/ # Core functionality │ └── extensions/ # Extended features └── validations/ # Validation criteria ``` ### File Naming ```python naming_pattern = { 'concepts': 'concept_{category}_{name}.md', 'implementations': 'impl_{system}_{component}.md', 'validations': 'val_{type}_{target}.md' } ``` ## Processing Instructions ### 1. Priority Levels ```yaml processing_priority: P1: "Critical path concepts" P2: "Core dependencies" P3: "Supporting information" P4: "Examples and extensions" P5: "Additional context" ``` ### 2. Processing Directives ```markdown #PROCESS_MODE: sequential|parallel #DEPENDENCY_CHECK: strict|flexible #VALIDATION_LEVEL: basic|complete ``` ### 3. Resource Management ```yaml resource_requirements: memory: "4GB" processing_time: "30s" api_calls: 10 ``` ## Validation Framework ### 1. Consistency Checks ```python # @consistency_check def verify_documentation(): """ Verify: 1. Link integrity 2. Semantic consistency 3. Implementation alignment """ pass ``` ### 2. Completeness Metrics ```yaml completeness_criteria: concepts: 0.95 implementations: 0.90 validations: 0.85 cross_references: 0.80 ``` ### 3. Quality Assurance ```python # @quality_metrics { "clarity_score": 0.9, "completeness_score": 0.85, "consistency_score": 0.95 } ``` ## Integration Examples ### 1. Knowledge Integration ```python # 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 ```mermaid graph TD A[Parse Documentation] --> B[Extract Knowledge] B --> C[Build Graph] C --> D[Validate] D --> E[Integrate] ``` ### 3. Validation Flow ```mermaid graph LR A[Documentation] --> B[Static Analysis] B --> C[Semantic Validation] C --> D[Integration Testing] D --> E[Knowledge Verification] ``` ## Related Documentation - [[machine_readability]] - [[knowledge_graph_structure]] - [[validation_framework]] - [[ai_processing_guidelines]] ## References - [[documentation_standards]] - [[machine_learning_integration]] - [[knowledge_representation]] - [[validation_methods]] ## AI Integration Principles ### 1. Semantic Enhancement - Use AI for semantic tagging - Enhance cross-referencing - Improve searchability - Maintain knowledge graphs ### 2. Content Generation - AI-assisted summaries - Code documentation - Example generation - Explanation enhancement ### 3. Validation - Consistency checking - Link validation - Style compliance - Completeness verification ## AI Tools and Usage ### Semantic Processing ```python def process_semantics(content): """Process document semantics. Args: content: Document content Returns: Enhanced semantic structure """ # Implementation ``` ### Documentation Generation ```python def generate_docs(code): """Generate documentation from code. Args: code: Source code Returns: Generated documentation """ # Implementation ``` ## Best Practices ### 1. Content Generation - Review AI-generated content - Maintain human oversight - Ensure accuracy - Preserve style consistency ### 2. Semantic Enhancement - Validate relationships - Check cross-references - Verify completeness - Maintain context ### 3. Quality Control - Regular validation - Human review - Style compliance - Accuracy verification ## Integration Workflows ### 1. Documentation Creation 1. Initial content creation 1. AI enhancement 1. Human review 1. Final validation ### 2. Maintenance 1. Regular updates 1. AI validation 1. Link checking 1. Style verification ### 3. Integration 1. Tool setup 1. Workflow configuration 1. Quality checks 1. Continuous improvement ## AI Tool Configuration ### 1. Semantic Processing ```yaml semantic_config: model: gpt-4 temperature: 0.7 max_tokens: 1000 style: technical ``` ### 2. Documentation Generation ```yaml doc_config: model: codex style: detailed format: markdown include_examples: true ``` ## Quality Assurance ### 1. Validation Checks - Content accuracy - Style compliance - Link validity - Semantic correctness ### 2. Review Process - AI pre-check - Human review - Final validation - Version control ### 3. Maintenance - Regular updates - Tool upgrades - Process improvement - Quality monitoring ### Repository Lint and Link Validation To keep documentation machine- and human-friendly, enforce markdown lint and link health checks: ```bash # Build repository-wide inventory (JSON/CSV/TXT) and wikilink graph python3 docs/repo_docs/repo_scripts/list_file_directory.py --root . --output docs/repo_docs/repo_scripts/output # Analyze and remediate broken/ambiguous/missing backlinks across knowledge_base/ and docs/ python3 docs/repo_docs/repo_scripts/fix_links.py --root . --output docs/repo_docs/repo_scripts/output ``` Authoring rules to reduce lint warnings: - Add blank lines around headings, lists, and fenced code blocks - Prefer a single H1 per page following frontmatter - Keep lines reasonably short; use fenced blocks for long code ## Related Documentation - [[ai_semantic_processing]] - [[ai_validation_framework]] - [[documentation_standards]]