Based on Princeton University & IIT Delhi Research

The Science Behind AI Visibility

Research-Backed GEO Technology

Discover how peer-reviewed academic research from Princeton University and IIT Delhi at ACM KDD 2024 transformed understanding of content visibility in AI platforms. The GEO techniques they developed can increase citation rates by up to 41.5% across ChatGPT, Claude, Gemini, and other leading AI systems. We apply these research-backed strategies for our clients.

📖 Explore the Research →

The AI Revolution Changed Everything

Traditional SEO focused on ranking in Google's list of blue links. Then AI platforms like ChatGPT, Claude, and Gemini changed the game completely, replacing those lists with comprehensive answers that cite only select sources.

The Princeton-IIT Delhi research found that 67% of businesses became effectively invisible in these AI-generated responses, regardless of their Google ranking position. This invisibility crisis required a completely new approach, which the research team developed and we now implement.

Key Research Finding:

AI platforms use fundamentally different signals than traditional search engines to determine which content gets cited. Understanding and optimizing for these signals is the cornerstone of effective Generative Engine Optimization.

The Visibility Gap: Traditional vs. AI Search

Traditional Search

  • • Focuses on keyword optimization
  • • Values backlink quantity and quality
  • • Shows multiple results for user to choose from

AI Search

  • • Focuses on semantic understanding and factual accuracy
  • • Values authoritative language and structured content
  • • Generates one comprehensive response with only select citations
  • • New optimization signals require GEO techniques
86%

of knowledge-seeking queries now involve AI platforms

4-6

citations average per AI response (out of thousands of potential sources)

Introducing Generative Engine Optimization

GEO is a groundbreaking academic framework for optimizing visibility in AI-generated responses, presented at ACM KDD 2024 by researchers from Princeton University and IIT Delhi. This external research forms the foundation for the services we provide at getbool.

Research Authors: Pranjal Aggarwal (IIT Delhi), Vishvak Murahari (Princeton), Tanmay Rajpurohit (Independent), Ashwin Kalyan (Independent), Karthik Narasimhan (Princeton), and Ameet Deshpande (Princeton).

GEO Definition

Generative Engine Optimization is the science of enhancing content visibility within AI-generated responses through semantic structure, factual authority, and citation engineering techniques.

Research Methodology

The Princeton and IIT Delhi research team analyzed 10,000+ queries and responses across multiple AI platforms to identify the specific factors that influence which sources get cited and which remain invisible.

Key Findings

AI systems favor content with clear factual statements, authoritative language patterns, structured data, and semantic context that aligns with how LLMs process and integrate information.

Measuring AI Visibility Metrics

The Princeton-IIT Delhi research established new quantitative metrics to measure and optimize visibility in AI-generated responses. At getbool, we implement these metrics to help our clients.

Citation Rate

The frequency at which a source is directly cited in AI-generated responses relative to topic-relevant queries.

Formula:
Citation Rate = (Citations / Relevant Queries) × 100
14%

Industry average

39%

With GEO implementation

Content Influence Score

Measures how much a source's content is semantically reflected in AI-generated responses, even when not directly cited.

Components:
• Semantic similarity
• Key points adoption
• Factual alignment
+57%

Response Inclusion

+42%

Factual Attribution

+31%

Position Improvement

Proven GEO Techniques

The Princeton-IIT Delhi research team's experimental validation across 10,000+ queries and responses identified these high-impact optimization techniques that we implement at getbool.

Quotation Engineering

+41.5%

Strategic placement of highly quotable statements that AI systems prefer to cite directly.

Implementation Example:
According to research by Princeton University, "GEO techniques can increase AI citation rates by up to 41.5% when properly implemented with quotable factual statements."

Structured Data Implementation

+36.7%

Implementing Schema.org markup that enhances AI's understanding of content entities and relationships.

<script type="application/ld+json">
{ "@context": "https://schema.org", "@type": "TechArticle", "headline": "GEO Implementation Guide" }
</script>

Authority Enhancement

+32.3%

Using writing patterns and citation structures that align with AI's evaluation of source credibility.

Implementation Example:
Our meta-analysis of 1,500 AI-generated responses found that content with authoritative language patterns and explicit research citations has a 32.3% higher probability of being referenced in AI answers.

Factual Density Optimization

+29.8%

Increasing the ratio of verifiable, concise facts per paragraph to improve citation likelihood.

Implementation Example:
GEO-optimized content contains 3.4x more verifiable factual statements per paragraph than traditional content, significantly increasing the likelihood of selection by AI systems analyzing information credibility.