I’ve been experimenting with using AI tools to streamline the documentation process for technical projects. The goal is to reduce the manual effort while maintaining high-quality, comprehensive documentation.

Background

Traditional documentation workflows often suffer from:

  • Time-consuming manual writing
  • Inconsistent formatting and style
  • Outdated information due to maintenance overhead
  • Poor discoverability and organization

I wanted to test whether AI could help address these issues while maintaining quality standards.

Experiment Setup

Tools Used

  1. AI Writing Assistant: For generating initial drafts
  2. Code Analysis AI: For extracting documentation from code
  3. Jekyll + GitHub Pages: For automated publishing
  4. Custom Prompts: For consistent formatting and style

Process Designed

  1. Extract key information from code/requirements
  2. Use AI to generate structured documentation drafts
  3. Review and refine the AI-generated content
  4. Publish through automated Jekyll workflow
  5. Maintain through AI-assisted updates

Results So Far

Positive Outcomes

  • Speed: 60-70% faster initial documentation creation
  • Consistency: AI maintains consistent tone and structure
  • Comprehensiveness: AI rarely misses important sections
  • Code Examples: Automatically generates relevant examples

Challenges Encountered

  • Context Limitations: Large codebases require chunking
  • Technical Accuracy: Still needs human verification
  • Customization: Generic outputs need personalization
  • Learning Curve: Prompt engineering takes time to master

Key Learnings

Effective Prompting Strategies

  1. Structured Templates: Use consistent formats for different doc types
  2. Context Priming: Always provide background and audience info
  3. Iterative Refinement: Build documentation in stages
  4. Example-Driven: Include examples of desired output format

Best Practices Discovered

  • Start with AI for structure, then add human expertise
  • Use AI for tedious formatting and consistency tasks
  • Maintain human oversight for technical accuracy
  • Create reusable prompt templates for common doc types

Tools Integration

  • Jekyll collections work well for categorized content
  • GitHub Actions can automate AI-assisted updates
  • Version control allows tracking of AI vs human contributions
  • Markdown provides good balance of structure and simplicity

Current Status

The experiment is ongoing with these active developments:

  1. Template Refinement: Improving prompt templates based on usage
  2. Automation Pipeline: Building GitHub Actions for doc updates
  3. Quality Metrics: Developing ways to measure doc effectiveness
  4. User Feedback: Collecting feedback on AI-generated docs

Next Steps

Short Term (Next Month)

  • Finalize the core prompt templates
  • Set up automated quality checks
  • Create user feedback collection system
  • Document the complete workflow process

Medium Term (Next Quarter)

  • Integrate with code review process
  • Experiment with multi-language documentation
  • Build custom AI tools for specific documentation tasks
  • Measure impact on developer productivity

Long Term (Next Year)

  • Open source the complete workflow system
  • Develop training materials for other teams
  • Research advanced AI capabilities for documentation
  • Build community around AI-assisted documentation

Lessons for Future Projects

  1. Start Simple: Begin with basic AI assistance, add complexity gradually
  2. Human-AI Collaboration: Best results come from combining AI speed with human expertise
  3. Process Documentation: Document the AI workflow itself for reproducibility
  4. Continuous Improvement: Regular refinement of prompts and processes is essential
  5. Quality Gates: Maintain quality standards despite automation