Comprehensive research overview on AI, SEO, AIO, and LLMO – Image: Xpert.Digital
Large Language Model Optimization: How artificial intelligence is fundamentally changing the SEO industry
Large Language Model Optimization: How artificial intelligence is fundamentally changing the SEO industry
The research landscape surrounding AI search engine optimization and Large Language Model Optimization (LLMO) is developing rapidly. This comprehensive analysis illuminates the current state of research on all relevant aspects of this emerging field.
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Basic concepts and terminology
LLMO, GEO and related terms
Research reveals a variety of terms for optimizing content for AI systems. Large Language Model Optimization (LLMO) focuses on optimization for large language models such as GPT-4, Claude, or Gemini. Generative Engine Optimization (GEO) aims at optimization for generative search engines, while AI Optimization (AIO) serves as an umbrella term for all AI optimization measures.
A groundbreaking study from Princeton University introduced the term “Generative Engine Optimization” into the scientific literature and demonstrated that GEO strategies can increase the visibility of AI-generated responses by up to 40%. This research established, for the first time, a systematic framework for optimizing content for generative AI systems.
How modern AI models work
Current research shows that AI models function through pretraining, fine-tuning, and retrieval augmented generation (RAG). The grounding process is particularly relevant, where AI systems enrich their answers with real-time web data through live searches. Google uses embeddings and semantic similarity calculations to evaluate content on a passage-by-pass basis, rather than searching entire pages for keywords.
Ranking factors and visibility factors
Google AI Overviews Ranking Factors
Extensive studies identified seven main areas that influence Google AI Overviews:
- AI models (PaLM 2, MUM, Gemini)
- Core Ranking Systems (PageRank, BERT, helpful content)
- Databases (Knowledge Graph, Shopping Graph)
- Topic areas (YMYL categories)
- Search intent (informational, navigational, transactional)
- Multimedia elements
- Structured data
Research shows that websites with better Google rankings have a 25% chance of appearing as a source in AI Overviews. Interestingly, almost 90% of ChatGPT citations come from search results outside the top 20 rankings.
Brand visibility and mention factors
A comprehensive analysis of 75,000 brands by Ahrefs revealed significant correlations for visibility in AI Overviews:
- Brand Web Mentions: Strongest correlation (0.664)
- Brand Anchors: Second strongest correlation (0.527)
- Brand Search Volume: Third strongest correlation (0.392)
- Backlinks: Significantly weaker correlation (0.218)
This research shows that off-site factors are more important than traditional SEO metrics. Brands with the most web mentions receive up to 10 times more mentions in AI Overviews than the next quartile group.
Brand awareness and LLM visibility
Studies by Seer Interactive demonstrate a correlation of 0.18 between brand search volume and AI mentions. This correlation is the second strongest observed connection after Domain Rank (0.25). The research shows that brand awareness is relevant not only for people but also for LLMs.
Technical optimization approaches
Structured Data and Schema Markup
Current research shows that AI crawlers often fail to recognize JavaScript-injected structured data. GPTBot, ClaudeBot, and PerplexityBot cannot execute JavaScript and therefore miss dynamically generated content. Server-side rendering or static HTML is essential for AI visibility.
Particularly effective are:
- FAQ format for direct question answering
- How-to diagram for step-by-step instructions
- Product schema for e-commerce optimization
- Article schema for content tagging
llms.txt as the new standard
Research identifies llms.txt as an important guide for AI crawlers. Unlike robots.txt, this file is not used for blocking, but rather as a structured overview of important content, similar to an XML sitemap for Google.
Measurability and monitoring tools
New KPI development
Research shows a shift from traditional rankings to mention rates and reference rates. Success is no longer measured in positions 1-10, but in the probability of being cited in AI responses.
Monitoring platforms
Recent studies identify several specialized tools for AI visibility tracking:
- SE Ranking AI Visibility Tracker: Monitors brand mentions across various AI platforms
- Advanced Web Ranking: Provides AI brand visibility insights
- Marlon: Specifically developed for LLM Brand Visibility
- LLMO Metrics vs. Lorelight: Platforms for Generative Engine Optimization
Comparative studies between platforms
ChatGPT vs. Google Search
Experimental studies show significant differences in user behavior. ChatGPT users require less time on average for all tasks, without significant differences in performance. ChatGPT levels out search performance across different educational levels, whereas Google Search shows a positive correlation between education and search performance.
Platform-specific features
Research results show different preferences for AI platforms:
- ChatGPT Search: Prefers long-form content over brand product pages
- Perplexity: Tends to use authoritative sources such as Wikipedia and major news sites
- Google AI Overviews: Uses co-citation patterns and existing ranking signals
Future trends and developments
Digital Authority Management
New research approaches such as Digital Authority Management (DAM) are emerging as an interdisciplinary field. This holistic approach combines SEO, content marketing, PR, and branding to build digital authority for AI systems. The AI Visibility Pyramid structures optimization measures into five levels: content quality, structural optimization, semantic optimization, authority building, and context management.
Entity-based optimization
Research shows the growing importance of entity-based SEO compared to pure keyword optimization. AI systems increasingly work with entities and their relationships, which signifies a shift from keywords to semantic concepts.
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Challenges and limitations
Determinism and measurability
Current research shows that AI responses are not deterministic – the same questions can generate different answers. This significantly complicates success measurement, as traditional SEO metrics are no longer applicable.
Rapid technological change
Research warns of the speed of technological change. Strategies that work today could quickly become obsolete due to model updates. This necessitates continuous adaptation and a willingness to experiment.
Practical insights
Content strategies
Research shows that topic coverage and holistic topic coverage are crucial. AI models favor content that can answer multiple sub-questions of a complex query through query fan-out.
EEAT in the context of AI
Studies show that Experience, Expertise, Authoritativeness, Trustworthiness (EEAT) remains relevant for AI systems. AI platforms prefer reliable, authoritative sources to minimize hallucinations.
AI optimization becomes a competitive advantage: Early investments in LLMO pay off
Current research shows that AI-powered SEO and LLMO are established as independent disciplines. While many traditional SEO principles remain relevant, AI systems require new approaches to content structuring, brand building, and technical implementation. Research is still in an experimental phase, but early investments in AI optimization promise long-term competitive advantages.
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