# Documentation Roadmap

---
title: Documentation Roadmap
type: roadmap
status: stable
created: 2024-02-06
tags:
  - roadmap
  - planning
  - documentation
  - maintenance
semantic_relations:
  - type: implements
    links: [[documentation_standards]]
  - type: relates
    links:
      - [[knowledge_organization]]
      - [[ai_documentation_style]]
      - [[content_management]]
---

## Overview

This roadmap outlines the strategic direction for maintaining and enhancing the Cognitive Modeling documentation framework. It provides a structured approach to documentation development, maintenance, and evolution.

## Current Documentation Structure

### Core Documentation
```
docs/
├── guides/           # Implementation guides and tutorials
├── concepts/         # Core theoretical concepts
├── api/             # API reference documentation
├── templates/        # Documentation templates
├── research/        # Research documentation
├── tools/           # Development tools documentation
└── examples/        # Usage examples
```

## Documentation Standards

### 1. File Organization
- Follow [[ai_file_organization]] for consistent structure
- Implement [[naming_conventions]] for all files
- Maintain [[linking_completeness]] across documents

### 2. Content Management
- Apply [[content_management]] guidelines
- Follow [[ai_documentation_style]] for formatting
- Ensure [[machine_readability]] for AI processing

### 3. Quality Assurance
- Regular validation using [[ai_validation_framework]]
- Link integrity checks via [[linking_validation]]
- Content analysis through [[ai_semantic_processing]]

## Maintenance Schedule

### Daily Tasks
- Monitor and fix broken links
- Update documentation for new code changes
- Review and address documentation issues

### Weekly Tasks
- Validate documentation completeness
- Update examples with new use cases
- Review and enhance API documentation

### Monthly Tasks
- Comprehensive documentation review
- Update roadmap and priorities
- Enhance machine-readable features
- Integrate new documentation tools

## Enhancement Priorities

### 1. Content Quality
- [ ] Enhance semantic relationships between documents
- [ ] Improve code example coverage
- [ ] Expand theoretical foundations documentation
- [ ] Add more interactive examples

### 2. Technical Infrastructure
- [ ] Implement automated documentation testing
- [ ] Enhance documentation generation tools
- [ ] Improve search and discovery features
- [ ] Develop documentation analytics

### 3. User Experience
- [ ] Create interactive documentation guides
- [ ] Enhance navigation and cross-referencing
- [ ] Improve documentation accessibility
- [ ] Add more visual documentation elements

## Implementation Timeline

### Q1 2024
1. Documentation Framework Enhancement
   - Improve semantic linking
   - Enhance validation tools
   - Update style guidelines

2. Content Development
   - Expand core concepts
   - Add advanced tutorials
   - Create video documentation

### Q2 2024
1. Technical Infrastructure
   - Automated testing
   - Enhanced search
   - Analytics dashboard

2. User Experience
   - Interactive guides
   - Visual documentation
   - Accessibility improvements

## Validation Framework

### 1. Documentation Quality
```python
quality_metrics = {
    "completeness": {
        "required_sections": 1.0,    # All required sections present
        "optional_sections": 0.8,    # 80% optional sections covered
        "code_examples": 0.9         # 90% code example coverage
    },
    "accuracy": {
        "technical_accuracy": 1.0,   # Technical content accuracy
        "code_correctness": 1.0,     # Code example correctness
        "link_validity": 0.95        # Link integrity
    },
    "readability": {
        "clarity": 0.9,             # Content clarity
        "structure": 0.95,          # Document structure
        "formatting": 1.0           # Formatting consistency
    }
}
```

### 2. Machine Readability
```python
machine_metrics = {
    "semantic_markup": {
        "metadata_completeness": 1.0,
        "relationship_clarity": 0.9,
        "processing_hooks": 0.85
    },
    "ai_processing": {
        "parse_success": 0.95,
        "context_preservation": 0.9,
        "knowledge_integration": 0.85
    }
}
```

## Integration Points

### 1. Development Workflow
- Integration with code review process
- Documentation-driven development
- Automated documentation updates

### 2. Research Integration
- Research paper documentation
- Experiment documentation
- Results documentation

### 3. Educational Resources
- Tutorial development
- Learning path creation
- Interactive examples

## Best Practices

### 1. Documentation Development
- Start with concept documentation
- Follow with implementation guides
- Include practical examples
- Add validation tests

### 2. Maintenance
- Regular review cycles
- Version control integration
- Automated validation
- User feedback integration

### 3. Evolution
- Continuous improvement
- Technology adaptation
- User needs assessment
- Documentation metrics

## Success Metrics

### 1. Documentation Coverage
- 100% API documentation
- 90% code example coverage
- 95% concept documentation
- 85% advanced topics

### 2. Quality Metrics
- 95% documentation accuracy
- 90% user satisfaction
- 85% automated test coverage
- 80% search effectiveness

### 3. Usage Metrics
- Documentation access rates
- Search success rates
- User engagement levels
- Feedback incorporation

## Related Documentation
- [[documentation_standards]]
- [[ai_documentation_style]]
- [[content_management]]
- [[validation_framework]]

## References
- [[theoretical_foundations]]
- [[machine_readability]]
- [[implementation_patterns]]
- [[quality_metrics]]