How I Built My Jekyll Blog: Automation, AI, and GitHub Actions Workflow

Building and maintaining a technical blog can be time-consuming, but with the right tools and automation, it becomes an efficient and enjoyable process. In this post, I’ll share my complete workflow for creating this Jekyll blog, from initial setup to automated deployment and AI-assisted content creation.

The Tech Stack

My blog runs on a modern, efficient stack that prioritizes speed, automation, and developer experience:

Jekyll: Static site generator that transforms Markdown into HTML
GitHub Pages: Free hosting with automatic deployment
GitHub Actions: CI/CD pipeline for automated testing and deployment
Chirpy Theme: Clean, responsive theme optimized for technical content
AI Assistance: Custom prompts with Grok for content creation and research

Initial Setup and Configuration

Jekyll Installation and Theme Setup

I started with a clean Jekyll installation using the Chirpy theme, which provides excellent defaults for technical blogging:

# Install Jekyll and dependencies
gem install jekyll bundler

# Create new Jekyll site with Chirpy theme
jekyll new my-blog --blank
cd my-blog

# Add Chirpy theme and dependencies to Gemfile
echo 'gem "jekyll-theme-chirpy", "~> 7.0", :group => [:jekyll_plugins]' >> Gemfile
bundle install

Directory Structure

My blog follows Jekyll’s conventional structure with some customizations:

dev-blog/
├── _posts/           # Blog posts in Markdown
├── _layouts/         # Custom page layouts
├── _includes/        # Reusable HTML components
├── assets/           # CSS, JS, images
├── _config.yml       # Site configuration
├── _data/           # Site data files
└── .github/         # GitHub Actions workflows

GitHub Actions Automation

Automated Deployment Pipeline

The heart of my workflow is a comprehensive GitHub Actions pipeline that handles everything from testing to deployment:

# .github/workflows/deploy.yml
name: Deploy Jekyll site

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  build-and-deploy:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout
        uses: actions/checkout@v4

      - name: Setup Ruby
        uses: ruby/setup-ruby@v1
        with:
          ruby-version: "3.1"
          bundler-cache: true

      - name: Build with Jekyll
        run: |
          bundle exec jekyll build
          bundle exec jekyll doctor

      - name: Test HTML
        run: |
          bundle exec htmlproofer ./_site \
            --disable-external \
            --check-html \
            --check-opengraph \
            --report-missing-names \
            --report-missing-alt-tags

      - name: Deploy to GitHub Pages
        if: github.ref == 'refs/heads/main'
        uses: peaceiris/actions-gh-pages@v3
        with:
          github_token: $
          publish_dir: ./_site

Key Automation Features

Automated Testing: Every push triggers HTML validation, link checking, and accessibility tests Dependency Updates: Dependabot keeps Ruby gems and GitHub Actions up to date Performance Monitoring: Lighthouse CI integration for performance tracking SEO Validation: Automated checks for meta tags, Open Graph, and structured data

AI-Assisted Content Creation Workflow

Custom Prompt Engineering

I use carefully crafted prompts to distill complex technical information into digestible blog content. Here’s my template for AWS certification content:

You are an expert AWS consultant and technical writer. Create a comprehensive blog post about [TOPIC] for the AWS [CERTIFICATION] exam.

Context: This is for developers preparing for certification. Focus on practical understanding over memorization.

Structure the post with:
1. Clear explanations with real-world examples
2. Code snippets where applicable
3. Visual diagrams or flowcharts
4. Common pitfalls and best practices
5. Study tips and resources

Key requirements:
- Use simple language, avoid jargon or explain it
- Include hands-on examples
- Cover exam objectives thoroughly
- Make it engaging and memorable
- End with next steps and resources

Topic: [SPECIFIC_DOMAIN_TOPIC]
Target audience: [EXPERIENCE_LEVEL]
Word count: [APPROXIMATE_COUNT]

Information Distillation Process

Research Phase: Gather information from AWS documentation, whitepapers, and official guides
Prompt Refinement: Craft specific prompts targeting the exact domain knowledge needed
AI Generation: Feed distilled information to Grok with custom prompts
Review & Edit: Human oversight to ensure accuracy and clarity
Visual Enhancement: Add diagrams, screenshots, and code examples

Example: AWS AI Practitioner Domain 1

For the recent Domain 1 post, I used this approach:

Research sources:
- AWS AI Practitioner exam guide
- Machine learning fundamentals documentation
- Real-world ML implementation examples

Custom prompt focus:
- Explain complex concepts with simple analogies
- Include PTOP (Point-to-Point) and KTOP (K-top) analysis
- Add practical examples for each algorithm
- Cover evaluation metrics with real scenarios

Content Creation Pipeline

Markdown-First Approach

All content starts as Markdown, which Jekyll renders beautifully:

---
layout: default
title: "AWS AI Practitioner Domain 1: Fundamentals"
date: 2025-11-05 10:00:00 +0000
categories: [aws, certification, ai]
tags: [aws, ai, machine-learning, exam-prep]
---

# Post Title

## Section 1

Content with **bold** and _italic_ text.

## Section 2

- Bullet points
- Code examples
- Links and references

```bash
# Code blocks with syntax highlighting
echo "Hello, World!"
```

### Screenshot Integration

When creating technical posts, screenshots are crucial for clarity:

1. **Development Screenshots**: Show code editors, terminal output, AWS console
2. **Diagram Creation**: Use tools like draw.io or Lucidchart for architecture diagrams
3. **Workflow Visualization**: Document the automation pipeline steps

### SEO and Performance Optimization

**Front Matter Configuration**:
```yaml
---
title: "SEO-Friendly Title"
description: "Meta description under 160 characters"
excerpt: "Longer excerpt for social sharing"
tags: [primary, secondary, tertiary]
---

Image Optimization:

WebP format for faster loading
Responsive images with multiple sizes
Lazy loading for performance
Alt text for accessibility

Development Workflow

Local Development Environment

# Start local Jekyll server with live reload
bundle exec jekyll serve --livereload

# Build for production
JEKYLL_ENV=production bundle exec jekyll build

Version Control Strategy

Branching Model:

main: Production-ready code
feature/*: New features and content
hotfix/*: Urgent fixes

Commit Convention:

feat: add new blog post about Jekyll workflow
fix: correct typo in AWS certification guide
docs: update README with new setup instructions
refactor: optimize GitHub Actions workflow

Monitoring and Analytics

Performance Tracking

GitHub Actions Integration:

Lighthouse CI for performance scores
Bundle analyzer for asset optimization
SEO audits on every deployment

Custom Analytics:

Google Analytics 4 for user behavior
Search Console for SEO performance
Social media engagement tracking

Content Performance

Key Metrics Tracked:

Page views and unique visitors
Average session duration
Popular content and referral sources
Search engine rankings

Challenges and Solutions

Common Issues Faced

Jekyll Build Failures:

Solution: Comprehensive CI testing catches issues before deployment
Prevention: Local testing before pushing changes

Content Consistency:

Solution: Standardized templates and style guides
AI assistance maintains consistent tone and quality

Performance Degradation:

Solution: Automated performance monitoring and optimization
Image optimization and caching strategies

Scaling Considerations

Content Volume: GitHub Actions handles unlimited posts efficiently Traffic Spikes: GitHub Pages scales automatically SEO Maintenance: Automated checks ensure ongoing optimization

Future Enhancements

Planned Improvements

Advanced Automation:

Automated content scheduling
Social media cross-posting
Newsletter integration

Enhanced AI Integration:

Automated content suggestions
SEO optimization assistance
Multi-language content generation

Performance Optimizations:

CDN integration for global distribution
Advanced caching strategies
Progressive Web App features

Lessons Learned

Key Takeaways

Automation is Essential: GitHub Actions eliminates manual deployment tasks
AI Augments, Doesn’t Replace: Human oversight ensures quality and accuracy
Start Simple, Scale Gradually: Begin with basic setup, add complexity as needed
Documentation Matters: Well-documented workflows are easier to maintain
Community Resources: Leverage existing themes and tools to accelerate development

Recommendations for Others

Getting Started:

Choose Jekyll for its simplicity and GitHub Pages integration
Start with a proven theme like Chirpy
Set up GitHub Actions early for automated deployment
Use AI tools for content creation, but maintain editorial control

Best Practices:

Test locally before deploying
Use descriptive commit messages
Keep dependencies updated
Monitor performance regularly
Backup important content

Conclusion

Building this blog has been an incredible learning experience that combines development, automation, and content creation. The workflow I’ve developed allows me to focus on writing and research while automation handles the technical heavy lifting.

The integration of AI assistance with traditional development practices has made content creation more efficient and consistent. GitHub Actions ensures reliable deployment and quality assurance.

If you’re considering building your own technical blog, I highly recommend this stack. It’s powerful yet approachable, and the automation makes maintenance virtually effortless.

Have you built your own blog or technical site? I’d love to hear about your workflow and tools in the comments!

This post was created using the exact workflow described above - AI-assisted content creation, Jekyll rendering, and automated deployment via GitHub Actions.