Lessons from AIEO-Checker Development Failure: Content Optimization in the Era Where AI Joined Your Readers

Introduction: Background of the AIEO-Checker Project

On June 19, 2025, we embarked on developing "AIEO-Checker," a tool designed to quantitatively measure the effectiveness of AIEO (AI Engine Optimization). The tool aimed to score how well web pages are understood by AI and provide improvement suggestions.

Using Claude Code for development, we went from prototype implementation to problem discovery to the conclusion of "failure" in just one day. This incredible speed of trial and error enabled us to gain insights instantly that would have traditionally taken weeks to discover.

This article shares the valuable insights gained from this rapid failure.

AIEO-Checker Concept and Implementation Approach

Initial Concept

AIEO-Checker was planned to evaluate the following elements:

Presence and Quality of Structured Data

Content Clarity

Credibility Indicators

Technical Optimization

Implementation Attempt

javascript

// Initial scoring logic (simplified)
function calculateAIEOScore(pageData) {
  let score = 0;
  
  // Check structured data
  if (pageData.hasStructuredData) score += 20;
  
  // Check for citations
  if (pageData.hasCitations) score += 15;
  
  // Evaluate heading structure
  if (pageData.hasProperHeadings) score += 10;
  
  // ... other evaluation items
  
  return score;
}

Fundamental Challenges Encountered

1. Different Evaluation Criteria by Page Type

The first problem we encountered was that important evaluation items vary significantly by page type.

Example: Presence of Citations

News Articles: Citations are important for credibility (+15 points)
Product Pages: Citations unnecessary, specifications more important
About Us Pages: Achievements and data more important than citations
Blog Posts: Citation importance varies by content

javascript

// Problem with uniform evaluation regardless of page type
if (hasCitations) {
  score += 15; // Same points for all pages... is this right?
}

2. Difficulty in Context-Dependent Evaluation

Example: Content Length

The optimal value for "content length" differs based on page purpose:

Detailed Technical Articles: 3000+ words preferred
FAQ Items: Concise 200-300 words optimal
Product Overview: Balanced around 1000 words

3. Black Box Nature of AI Evaluation Criteria

The most challenging aspect was that we cannot fully know what criteria AI uses to evaluate pages.

Gap Between Our Assumptions and Reality

javascript

// Weighting based on our assumptions
const weights = {
  structuredData: 0.3,    // 30% importance?
  contentClarity: 0.25,   // 25% importance?
  citations: 0.2,         // 20% importance?
  technicalSEO: 0.25      // 25% importance?
};

// However, actual AI evaluation criteria...
// - Varies by model
// - Changes dynamically with context
// - Cannot be perfectly replicated

4. Ambiguity of Overall Score Meaning

The limitations of expressing everything with a single score became clear:

Page A with score 85: Perfect structure but thin content
Page B with score 85: Average structure but rich content

Same score, but completely different improvement points.

Important Insights from Failure

1. Limitations of Uniform Scoring

AIEO cannot be evaluated with a uniform checklist like traditional SEO. Optimization direction varies greatly depending on page purpose, target audience, and content type.

2. Importance of Understanding Context

markdown

❌ Bad Approach:
"Adding citations to all pages will increase the score"

✅ Good Approach:
Consider "What is this page's purpose? What are readers looking for?"
then determine necessary elements

3. Addressing AI Diversity

Different AI models (GPT-4, Claude, Gemini, etc.) have different characteristics:

GPT-4: Emphasizes structured data
Claude: Values context consistency
Gemini: Utilizes multimodal information

A single evaluation criterion cannot address this diversity.

Recommendations for Future AIEO Strategies

1. Page Type-Specific Optimization Strategies

News/Blog Articles

Emphasize credibility indicators (author, date, citations)
Implement structured data (Article schema)
Clear heading structure

Product/Service Pages

Structure specification information
Enrich FAQ sections
Utilize user reviews

Company Information Pages

Structure organization information (Organization schema)
Display achievement data clearly
Manage update history

2. Continuous Improvement Approach

javascript

// Checklist approach instead of scoring
const aieoChecklist = {
  'productPage': [
    'Implement structured data (Product schema)',
    'Clear specification descriptions',
    'FAQ section setup',
    'Links to related products'
  ],
  'blogPost': [
    'Display author information',
    'Show publication/update timestamps',
    'Proper heading structure',
    'Links to related articles'
  ]
  // Different checklists for each page type
};

3. Importance of Qualitative Evaluation

Elements that cannot be quantified are also important:

Content originality
Value provision to readers
Information accuracy and currency

Conclusion: What We Learned from Failure

The development of AIEO-Checker ended in technical failure. However, the insights gained from this experience are extremely valuable for future AIEO strategies.

Key Points

There's No Universal Solution for AIEO

Context is Everything

Continuous Experimentation and Improvement

Don't Lose Sight of the Essence

Final Thoughts

Failure is the first step to success. The insights gained from AIEO-Checker development became the foundation for more practical and effective AIEO strategies.

Rather than pursuing uniform scores, optimize according to each page's purpose. We believe this is the path to true AIEO success.

We hope your AIEO strategies can gain something from this failure story.