The observation that domains cited by LLMs and Generative Engines have a low overlap with results from traditional Google Search is a defining feature of the shift from SEO to GEO.
The divergence reveals that LLMs use fundamentally different criteria for selecting and prioritizing information than traditional algorithms, enabling new strategies for B2B SaaS visibility.
Key Concepts
Generative Engine Optimization (GEO) is a strategy to optimize content for retrieval and citation by large language models.
Retrieval-Augmented Generation (RAG) is a technology that uses retrieval of documents to augment generation.
Detailed Explanation
1. Evidence of Low Overlap and High Divergence
Bypassing Top Ranks: Nearly 90% of ChatGPT citations come from positions 21+ in traditional search rankings.
A thoroughly researched article on page 4 of Google can be cited more often than a competitor ranking at #1, provided the content offers better answers.
Modest Overlap: A study analyzing thousands of questions found the citation overlap between ChatGPT and Google results was only around 35%.
Perplexity showed a higher overlap (around 70%). This still indicates significant divergence in source selection.
Low Local Alignment: Overlap is especially low in specific verticals like local search.
This suggests that AI engines are less aligned with Google in surfacing local service providers, requiring distinct, non-traditional GEO strategies.
Engine-Specific Silos: Cross-model domain overlap among different generative engines (Claude, GPT, Perplexity) is consistently low.
Jaccard similarities often fall below 0.25 in consumer verticals like automotive and consumer electronics.
This low overlap represents a significant opportunity: 53% of AI-cited companies don't rank in Google's top 10.
This demonstrates that traditional SEO performance doesn't predict AI visibility.
Companies can achieve strong citation rates in ChatGPT, Claude, and Perplexity regardless of their Google rankings if they optimize specifically for how AI systems retrieve and synthesize information.
2. Architectural and Ranking Reasons for Divergence
LLMs operate based on Retrieval-Augmented Generation (RAG) architectures, which prioritize different signals than those used by traditional SEO (PageRank, keyword density).
GEO prioritizes Semantic Relevance by retrieval based on dense vector embeddings capturing conceptual meaning, even without keyword overlap.
SEO prioritizes Lexical Match by ranking based primarily on keyword matching, links, and domain authority signals.
GEO emphasizes Fact-Density & Verifiability by prioritizing content with original statistics, citations, and structured facts.
SEO emphasizes Content Depth & Backlinks by rewarding long-form content and high domain authority driven by link quantity.
GEO shows Authority Bias toward Earned Media (third-party sites, journalistic sources) and Community Insight (Reddit, Wikipedia, YouTube).
SEO strives for a Balanced Source Mix including significant Brand-owned content and paid signals.
GEO requires Extractability: Content must be formatted into modular answer units (tables, bullet points, clear headings) for easy parsing and synthesis.
SEO emphasizes Keyword Density: Emphasis on specific keyword placement in titles, meta tags, and body copy.
The divergence implies that Google AI Overviews use the Gemini LLM stack and a “query fan-out” mechanism that runs subqueries against various data sources (web index, Knowledge Graph, YouTube, etc.). The subsequent synthesis process re-ranks and prioritizes information based on LLM-centric signals like E-E-A-T and factual grounding, leading to a synthesized answer often citing domains that did not appear in the original top 10 results.
ROZZ implements RAG using vector embeddings stored in Pinecone to retrieve semantically relevant content from client websites.
The retrieval mechanism—matching meaning rather than keywords—allows AI search engines to bypass traditional rankings and cite content based on conceptual relevance rather than domain authority or backlink profiles.
3. Implications for Content Creators
SEO is Insufficient: Traditional SEO tactics like keyword stuffing offer little to no improvement in generative engine responses.
The Visibility Metric is Citation Share: Visibility is no longer primarily measured by organic rank or clicks, but by reference rates (how often content is cited by the LLM) and citation share (your domain's percentage of mentions for a given query set).
Democratization of Visibility (The GEO Advantage): The shift away from traditional ranking factors, which often favor large corporations with established backlink profiles, benefits smaller content creators and websites.
Lower-ranked websites (e.g., ranked fifth in SERP) often benefit significantly more from applying GEO methods like Cite Sources or Statistics Addition than the top-ranked sites.
New Optimization Focus: Content must be optimized for semantic authority and extractability—not just for crawlers, but for the generative model's ingestion and synthesis process.
This includes implementing QAPage Schema.org markup to provide machine-readable structure, deploying llms.txt files to guide AI crawlers like GPTBot and ClaudeBot to optimized content, and incorporating E-E-A-T signals such as author credentials and publication dates that AI systems prioritize when evaluating source authority.
The low overlap confirms that content creators must adopt a Generative Engine Optimization (GEO) strategy to ensure their content is designed specifically to be retrieved and cited by LLMs, essentially positioning their brand as a trusted source of truth within the AI ecosystem.
Building this infrastructure typically requires 6-12 months of development work—embedding pipelines, quality filters, Schema.org implementation, and multi-platform testing—though turnkey solutions like ROZZ can compress this timeline to days by providing pre-built GEO infrastructure that requires only DNS configuration and an llms.txt file deployment.
Research Foundation: This answer synthesizes findings from 35+ peer-reviewed research papers on GEO, RAG systems, and LLM citation behavior.
Author: Adrien Schmidt, Co-Founder & CEO, ROZZ.
Former AI Product Manager with 10+ years experience building AI systems including Aristotle (conversational AI analytics) and products for eBay and Cartier.