AI Development Reality Check: Lessons from Blog Automation

AI Development Reality The reality of AI development: coexistence of convenience and complexity

📝 Today’s Problem

For the past few days, I’ve been completely immersed in creating a GitHub Pages blog and working on AI prompt automation.

Since AI was generating code so smoothly, I thought “Wow, this is really easy!” But when I actually tried to run it, problems kept popping up everywhere.

💡 Problem-Solving Process

AI’s Strengths: Rapid Prototyping

# Jekyll configuration generated by AI in 1 minute
plugins:
  - jekyll-feed
  - jekyll-sitemap
  - jekyll-seo-tag

collections:
  posts:
    output: true
    permalink: /:year/:month/:day/:title/

What was great: AI creates basic structures and templates really quickly

AI’s Limitations: Detailed Debugging

# In reality, these errors kept occurring
Error: Liquid syntax error: Unknown tag 'mermaid'
Error: Github Pages build failed

The problem: When AI-generated code doesn’t work in the actual environment, finding what went wrong is ultimately the developer’s responsibility

🎯 Key Insights

1. AI + Existing Knowledge: An Essential Combination

AI generates code, but guiding it in the right direction is the developer’s role
Existing knowledge to validate and modify the generated output is essential

2. The New Dilemma for Junior Developers

// Old junior developer's goal
const juniorGoal = "Step by step from Hello World";

// Current junior developer's reality
const currentReality = "This much with AI should be basic, right?";

Paradoxical situation:

Learning has become easier, but expectations have risen to 5-6 years of experience level
AI tool utilization skills are now additionally required

3. Core Skills in AI Development

Prompt Engineering: Accurately conveying requirements to AI
Output Validation: Identifying problems in generated code
Iterative Improvement: Progressively enhancing quality together with AI

📈 Conclusion

AI development is an innovation in tools, not a replacement for development knowledge.

In fact, you need existing development knowledge to properly utilize AI and to judge “this isn’t right” when AI makes mistakes.

Essential competencies for new developers:

Traditional development knowledge (fundamentals)
AI tool utilization skills (new fundamentals)
Insight to connect these two domains

It’s quite ironic, but this is reality. Developers in the AI era need to know even more.