Comprehensive research overview of KI, SEO, AIO, and LLMO - Image: Xpert.digital
Large Language Model Optimization: How artificial intelligence fundamentally changed the SEO industry
Large Language Model Optimization: How artificial intelligence fundamentally changed the SEO industry
The research landscape around 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 area.
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Basic concepts and terminology
Llmo, geo and related terms
Research shows a variety of terms for optimizing content for AI systems. Large Language Model Optimization (LLMO) focuses on optimization for large voice models such as GPT-4, Claude or Gemini. Generative Engine Optimization (GEO) aims to optimize generative search engines, while AI optimization (AIO) serves as a generic term for all AI optimization measures.
A pioneering study by Princeton University introduced the term “generative engine optimization” and demonstrated that geo-strategies can increase visibility in AI generated answers by up to 40%. For the first time, this research established a systematic framework for optimizing content for generative AI systems.
How modern AI models
Current research shows that AI models work through pret training, fining tuning and retrieval-Augmented generation (RAG). The grounding process is particularly relevant, in which AI systems enrich your answers by looking for live data. Google uses embeddings and semantic similarity calculations to assess content passages instead of 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)
- Subject areas (Ymyl categories)
- Search intention (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. It is interesting that almost 90% of the chatt quotes come from search results beyond the top 20 rankings.
Fire visibility and mentioned mentioned mentioned mentioned in mentions
A comprehensive analysis of 75,000 brands by Ahrefs showed significant correlations for visibility in AI Overviews:
- Brand web mentions: strongest correlation (0.664)
- Anchors fire: the 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 awareness receive up to 10x 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 fire search volume and AI mention. According to Domain Rank (0.25), this correlation is the second strongest observed connection. Research shows that brand awareness is not only relevant for humans, but also for LLMS.
Technical optimization approaches
Structured data and scheme Markup
Current research shows that AI crawler often cannot recognize JavaScript-injected structured data. Gptbot, Claudebot and Perplexitybot cannot run JavaScript and miss a dynamically generated content. Server-side rendering or static HTML is essential for ai visibility.
Are particularly effective:
- FAQ scheme for direct questionnaire
- Howto Scheme by step-by-step instructions
- Product scheme for e-commerce optimization
- Article scheme for content marking
llms.txt as the new standard
Research identifies llms.txt as an important guide for AI crawler. Unlike Robots.txt, this file does not serve to block, but as a structured overview of important content, similar to an XML siteemap for Google.
Measurability and monitoring tools
New KPI development
Research shows a shift in traditional rankings to mention rates and reference councils. Success is no longer measured in positions 1-10, but in the probability of being cited in AI answers.
Monitoring platforms
Current studies identify various specialized tools for AI visibility tracking:
- Se ranking ai Visibility Tracker: monitors brand mention in various AI platforms
- Advanced Web Ranking: offers AI Brand Visibility Insights
- Marlon: Developed especially for LLM Brand Visibility
- LLMO Metrics vs. Loright: Platforms for generative engine optimization
Comparison studies between platforms
Chatgpt vs. Google Search
Experimental studies show significant differences in user behavior. Chatgpt users need less time for all tasks, without significant differences in performance. Chatgpt levels the search performance between different levels of education, while on Google Search there is a positive correlation between education and search performance.
Platform-specific features
Research results show different preferences of the AI platforms:
- Chatgpt Search: prefers long-form content towards Brand Product Pages
- Perplexity: tends to authoritative sources such as Wikipedia and large news sites
- Google AI Overviews: Uses Co-Citation pattern and existing ranking signals
Future trends and developments
Digital Authority Management
New research approaches such as Digital Authority Management (DAM) are created as an interdisciplinary discipline. This combines SEO, content marketing, PR and branding holistically in order to build digital authority for AI systems. The AI visibility pyramid structures optimization measures in five levels: content quality, structural optimization, semantic optimization, authority building and context management.
Entity-based optimization
Research shows the growing meaning of entity-based SEO compared to pure keyword optimization. AI systems work increasingly with entities and their relationships, which means a shift in keywords to semantic concepts.
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Challenges and limitations
Determinism and measurability
Current research shows that AI answers are not deterministic-the same questions can generate different answers. This makes it difficult to measure success because traditional SEO metrics no longer apply.
Rapid Technological Change
Research warns of the speed of technological changes. Strategies that work today could quickly become obsolete through model updates. This requires continuous adaptation and joy of experimentation.
Practical knowledge
Content strategies
Research results show that topic coverage and holistic theme coverage are decisive. AI models prefer content that can answer several sub-questions of a complex request through query fan-out.
Eeat in the AI context
Studies show that experience, expertise, authoritativeness, trust, trusting (EEAT) also 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
The current research situation shows that KI SEO and LLMO are established as an independent disciplines. While many traditional SEO principles remain relevant, AI systems require new approaches in content structuring, fire building and technical implementation. Research is still in an experimental phase, with early investments in AI optimization promise long-term competitive advantages.
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