ADR-0035: Terminal-Centric Documentation Strategy

Status

Accepted

Context

The Qubinode Navigator project needs comprehensive documentation that serves multiple user personas and use cases:

  • New users deploying for the first time
  • Experienced users extending existing deployments
  • CI/CD pipeline integrators
  • Contributors and developers
  • Future Hugging Face showcase visitors

The documentation must work seamlessly with the terminal-based deployment architecture and AI Assistant integration, providing clear guidance that complements automated troubleshooting and post-deployment extension capabilities.

Current documentation is scattered across multiple files and lacks a cohesive user journey that integrates with the one-shot deployment approach and AI Assistant capabilities.

Decision

We will implement a terminal-centric documentation strategy that provides comprehensive guidance for the complete user journey, from initial deployment through advanced infrastructure extension, with integrated AI Assistant interaction patterns.

Core Documentation Principles:

  1. Terminal-First Approach: All documentation assumes terminal-based interaction
  2. Progressive Disclosure: Information organized by user experience level and use case
  3. AI Assistant Integration: Documentation shows how to leverage AI for guidance
  4. Executable Examples: All code examples are copy-pastable and tested
  5. Journey-Based Organization: Content organized around user workflows, not technical components
  6. Future-Ready Structure: Architecture supports Hugging Face showcase integration

Implementation Details:

Documentation Architecture: 

docs/
├── user-guides/
│   ├── quick-start.md              # New user 5-minute deployment
│   ├── deployment-guide.md         # Comprehensive deployment documentation
│   ├── ai-assistant-guide.md       # AI interaction patterns and examples
│   ├── post-deployment-guide.md    # Building on deployed infrastructure
│   └── troubleshooting-guide.md    # Common issues and AI-assisted resolution
├── reference/
│   ├── environment-variables.md    # Complete .env reference
│   ├── supported-platforms.md      # OS and cloud provider matrix
│   ├── api-reference.md           # AI Assistant API documentation
│   └── command-reference.md       # All available commands and scripts
├── examples/
│   ├── hetzner-deployment/        # Complete Hetzner deployment example
│   ├── local-development/         # Local development setup
│   ├── openshift-on-kvm/         # Post-deployment OpenShift example
│   └── ci-cd-integration/         # Pipeline integration examples
└── adrs/                          # Architectural Decision Records (existing)

User Journey Documentation:

  1. Discovery Phase: Quick-start guide with single-command deployment
  2. Deployment Phase: Comprehensive guide with AI Assistant integration
  3. Validation Phase: Verification steps and troubleshooting with AI
  4. Extension Phase: Building additional services on deployed infrastructure
  5. Maintenance Phase: Updates, backups, and ongoing management

AI Assistant Integration Documentation:

During Deployment: 
## Getting Help During Deployment

The AI Assistant automatically provides guidance when errors occur - no manual intervention needed:

```bash
# Automatic AI assistance during deployment
./deploy-qubinode.sh

# If any step fails, you'll automatically see:
[ERROR] Failed to install packages
[AI ASSISTANT] Analyzing error and providing troubleshooting guidance...
╔══════════════════════════════════════════════════════════════╗
║                    AI ASSISTANT GUIDANCE                     ║
╚══════════════════════════════════════════════════════════════╝
The package installation failure is likely due to...
[Step-by-step resolution guidance]

For more help, visit: http://localhost:8080

No curl commands needed - the AI Assistant is seamlessly integrated!


##### Post-Deployment Extensions:
```markdown
## Building on Your Infrastructure

Ask the AI Assistant for guidance on next steps:

```bash
# Deploy OpenShift
curl -X POST -H "Content-Type: application/json" \
  -d '{"message": "How do I deploy OpenShift 4.14 on my KVM infrastructure?"}' \
  http://localhost:8080/chat

# Add monitoring
curl -X POST -H "Content-Type: application/json" \
  -d '{"message": "What monitoring solutions work well with this setup?"}' \
  http://localhost:8080/chat

```

Documentation Standards:

  1. Executable Code Blocks: All examples must be copy-pastable and work
  2. Error Scenarios: Document common failure modes and AI-assisted resolution
  3. Prerequisites: Clear system requirements and validation steps
  4. Success Criteria: How to verify each step completed successfully
  5. Next Steps: Always provide clear progression paths

Consequences

Positive:

  • Reduced Onboarding Time: New users can deploy successfully in minutes
  • Self-Service Support: AI Assistant reduces need for human intervention
  • Consistent Experience: Standardized documentation patterns across all guides
  • Extensibility Focus: Clear guidance for building on deployed infrastructure
  • CI/CD Ready: Documentation supports automated deployment scenarios
  • Community Showcase Ready: Structure supports future Hugging Face integration

Negative:

  • Maintenance Overhead: Documentation must stay synchronized with code changes
  • AI Dependency: Some guidance assumes AI Assistant availability
  • Terminal Assumption: May not serve users preferring GUI interfaces

Neutral:

  • Learning Curve: Users must understand terminal and API interaction patterns
  • Content Volume: Comprehensive documentation requires significant initial effort

Implementation Status

  • ⏳ Quick-start guide creation
  • ⏳ AI Assistant interaction documentation
  • ⏳ Post-deployment extension guides
  • ⏳ Troubleshooting guide with AI integration
  • ⏳ Reference documentation updates
  • ⏳ Example scenarios and use cases

Documentation Content Plan

Phase 1: Core User Guides

  1. Quick-Start Guide (5-minute deployment)
    • Single command: ./deploy-qubinode.sh
    • Prerequisites check
    • Success validation
    • AI Assistant introduction
  2. AI Assistant Guide
    • API interaction patterns
    • Common queries and responses
    • Troubleshooting workflows
    • Post-deployment guidance examples

Phase 2: Extension Guides

  1. Post-Deployment Guide
    • OpenShift deployment on KVM
    • Additional VM provisioning
    • Network and security configuration
    • Monitoring and logging setup
  2. Advanced Use Cases
    • Multi-node deployments
    • Cloud provider integration
    • CI/CD pipeline setup
    • Backup and disaster recovery

Phase 3: Reference Materials

  1. Complete API Reference
  2. Environment Variable Documentation
  3. Platform Compatibility Matrix
  4. Troubleshooting Database

Quality Assurance

Documentation Testing:

  • All code examples tested on supported platforms
  • AI Assistant interactions validated
  • User journey walkthroughs with new users
  • Regular updates based on user feedback

Integration with Development:

  • Documentation updates required for all feature changes
  • ADR creation triggers documentation review
  • AI Assistant training includes documentation content

Dependencies (Required)

  • ADR-0033: Terminal-Based One-Shot Deployment Architecture - Defines deployment workflow to document
  • ADR-0034: AI Assistant Terminal Integration Strategy - Defines AI interaction patterns to document

Integrates With

  • ADR-0029: Documentation Strategy and Website Modernization - Provides broader documentation framework
  • ADR-0027: CPU-Based AI Deployment Assistant Architecture - Documents AI capabilities and use cases
  • ADR-0032: AI Assistant Community Distribution Strategy - Documents community distribution approach

Supports

  • ADR-0001: Container-First Execution Model - Documents containerized execution workflows
  • ADR-0026: RHEL 10/CentOS 10 Platform Support Strategy - Documents modern OS support procedures

Notes

This documentation strategy creates a comprehensive, AI-integrated user experience that guides users from initial deployment through advanced infrastructure extension. The terminal-centric approach ensures compatibility with the deployment architecture while preparing for future Hugging Face showcase integration.

The documentation serves as both user guidance and AI Assistant training material, creating a self-reinforcing knowledge system.