# aisearchoptimization.hu > Optimize for AI answer engines like ChatGPT and Perplexity. Measure citability, reduce hallucinations, and fix infrastructure for generative search. AI systems reconstruct experts rather than rank them. This site treats citability as an infrastructure problem requiring machine-readable, canonically anchored entities. ## Author - Zoltán Varga — AI visibility researcher and AI ecosystem architect, Budapest: https://vargazoltan.ai/ - English profile: https://zoltanvarga.ai/en/about/ - ORCID: https://orcid.org/0009-0003-1020-834X - arXiv author: https://arxiv.org/a/varga_z_1.html - Zenodo: https://zenodo.org/search?q=owners%3A1653886 ## Publications - Per-Entity Bias Mapping for AI Visibility: Why Brand Mentions Require Entity-Specific Calibration (arXiv:2606.21595): https://arxiv.org/abs/2606.21595 - AI Visibility in the Generative Era: A Manifesto for Entity-Level Measurement (DOI 10.5281/zenodo.20285424): https://doi.org/10.5281/zenodo.20285424 - Quality Challenges Do Not Escalate Source Fabrication in Large Language Models (RICE-P1) (DOI 10.5281/zenodo.20474050): https://doi.org/10.5281/zenodo.20474050 - Rejection-Induced Confabulation Escalation — Pilot Study 2 (RICE-P2) (DOI 10.5281/zenodo.20509755): https://doi.org/10.5281/zenodo.20509755 ## Key topics - AI search optimization - generative engine optimization - AEO strategy - LLMO techniques - ChatGPT citations - Perplexity optimization - Google AI Overviews - brand hallucination - AI visibility audit - machine-readable entities - stochastic search - GEO Hungary - AI search trends - optimizing for AI - answer engine SEO - AI citation errors - AI search visibility - generative AI audit ## Questions and answers ### How does AI search optimization differ from traditional SEO? Traditional SEO targets ten blue links with deterministic rankings. AI search optimization focuses on how models reconstruct entities and cite sources in generative answers. There are no logged clicks or impressions in this new environment. ### What is the PEBM study finding regarding AI hallucinations? The PEBM study ran 1,400 tests on 100 Hungarian B2B companies. It found that 45.9% of cited sources were fabricated overall, with higher rates for well-known firms. ### Why do well-known brands face more false attributions? The brand hallucination hypothesis suggests familiarity gives models more material for plausible completions. This leads to confident but unsupported attributions for famous entities. ### Can AI crawlers access all pages on a website? Access does not guarantee indexing. In one case, all pages returned HTTP 200 to bots, yet Google, Bing, and Brave indexed zero pages. ### Do search-based AI engines provide accurate dosage information? With search enabled, engines provided officially approved doses in all tested instances. Without search, some models stated incorrect doses, creating brand risks. ### Is there a single number to measure AI visibility? No single number exists because visibility is a distribution, not a point. Scores are proxies derived from multiple layers of output and processing. ## Related - AI visibility expert page: https://vargazoltan.ai/ai-lathatosagi-szakerto/ - AI visibility audit: https://vargazoltan.ai/audit/ - Research: https://vargazoltan.ai/kutatas/ ## Articles - [GEO, AEO, LLMO: Three Terms, Three Different Targets](https://aisearchoptimization.hu/articles/geo-aeo-llmo-defined/): GEO targets generative engines to secure citations within AI responses, while AEO optimizes content for direct answers in featured snippets or voice results. LLMO focuses on training data and model memory to ensure brands are recalled during complex reasoning tasks. These distinctions dictate different content structures, measurement metrics, and investment priorities. - [Why AI Citability Is Not 'the New SEO'](https://aisearchoptimization.hu/articles/citability-is-not-the-new-seo/): AI search optimization is not the new SEO because the underlying goal has shifted from ranking position to entity citability. Traditional SEO measures where you appear in a list, while AI visibility measures if you exist as a trusted source within the model's synthesis. This distinction marks a fundamental paradigm change, not a mere rebranding of existing tactics. - [From PageRank to LLMO: How Search Optimization Changed](https://aisearchoptimization.hu/articles/from-pagerank-to-llmo/): Search optimization evolved from optimizing for link-based rankings like PageRank to optimizing for AI citations in generative engines. The shift moved focus from click-through rates to visibility within Large Language Model outputs, requiring structured data and authoritative sourcing rather than traditional keyword density. - [Which SEO Practices Still Matter for AI Answers](https://aisearchoptimization.hu/articles/which-seo-practices-still-matter/): Traditional SEO practices like meta-tag optimization do not directly correlate with AI citation in ChatGPT or Perplexity. Answer engines prioritize domain authority, earned media mentions, and deep contextual content over HTML metadata. To maintain visibility, you must shift focus from keyword density to narrative coherence and entity consistency across your digital footprint. - [AEO or GEO First? Where the Two Disciplines Diverge](https://aisearchoptimization.hu/articles/aeo-or-geo-first/): Companies should prioritize foundational technical SEO and high-authority content before specializing in AEO or GEO, as generative engines rely on the same retrieval mechanisms as traditional search. The best return on investment comes from a unified system where structured data and clear answers serve both immediate answer boxes and long-form generative synthesis simultaneously. - [Local Businesses in AI Answers: GEO vs Local SEO](https://aisearchoptimization.hu/articles/local-business-geo-vs-local-seo/): Local businesses get recommended by AI assistants through entity citation and authoritative content rather than traditional keyword ranking. Unlike map-pack tactics, generative engines prioritize structured data accuracy and third-party endorsements to synthesize answers. Visibility depends on appearing in trusted external sources that models like ChatGPT and Gemini reference directly. - [Training Data or Live Search: Where Optimization Can Reach](https://aisearchoptimization.hu/articles/training-data-or-live-search-levers/): You cannot directly insert your brand into a large language model's training data after the model is released. Optimization efforts instead focus on retrieval-augmented generation layers and external knowledge graphs that models access during live queries. These retrieval-based interventions offer faster results and greater control than waiting for a model retraining cycle. - [Zero-Click and AI Overviews: What Happens to Organic Traffic](https://aisearchoptimization.hu/articles/zero-click-and-ai-overviews/): AI Overviews and zero-click searches have reduced organic click-through rates by approximately 61% for queries where they appear, according to 2025 data from Seer Interactive. While the overall zero-click rate sits around 58.5% in the US, the impact is uneven: brands cited within AI summaries see a 35% increase in organic clicks, while non-cited sites face significant traffic erosion. - [Durable vs Gameable: GEO Tactics That Survive Model Updates](https://aisearchoptimization.hu/articles/durable-vs-gameable-geo-tactics/): Manipulation tactics that attempt to force citations through keyword stuffing or artificial priming are neutralized as models update their bias-correction layers. Durable tactics focus on building authorial authority and structured data that aligns with the model's reliance on open-access, verifiable evidence bases. The goal is to become a trusted source within the model's retrieval layer rather than trying to hack the generation process. - [Earned Mentions and Digital PR as GEO Infrastructure](https://aisearchoptimization.hu/articles/earned-mentions-as-geo-infrastructure/): Yes, independent third-party mentions from trade media, reviews, and expert voices form the essential evidence layer that generative AI models retrieve to construct brand citations. These sources act as the ground truth for knowledge graphs, allowing AI systems to verify entity existence and authority before generating answers. Building this infrastructure requires consistent publication of structured, factual content rather than paid placement or astroturfing. - [GEO Vendor Due Diligence: 5 Questions to Vet AI Visibility Agencies](https://aisearchoptimization.hu/articles/geo-vendor-due-diligence-checklist/): To choose a reliable GEO or AI visibility agency, demand proof of repeated measurements rather than single-point scores and reject any vendor promising specific rankings or guaranteed citations. Verify that their method focuses on building authorial authority and structured data consistency instead of manipulating LLM outputs through artificial priming or keyword stuffing. - [GEO and AEO FAQ: Timelines, Myths and Practitioner Doubts](https://aisearchoptimization.hu/articles/geo-aeo-faq/): Results from Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) typically emerge within three to six months of consistent structural changes. Unlike traditional ranking shifts, AI visibility relies on repeated citations and entity recognition rather than immediate algorithmic updates. Small businesses often see initial brand mentions before measurable referral traffic increases.