What Is GEO? Generative Engine Optimization Explained (2026)

The search for information is changing fundamentally. More and more users turn directly to AI systems and expect a finished answer instead of a list of results.

For brands and companies, this brings new challenges: visibility no longer arises solely through rankings, but through whether content is taken into account in AI answers.

SEO remains important, but on its own it is no longer enough. This is exactly where Generative Engine Optimization (GEO) comes in.

This guide shows what GEO means, how AI systems select content, and which levers are decisive for being mentioned in AI chats or cited as a source.

Overview

• What is GEO? GEO refers to all measures that help websites, brands, and companies appear more often in the answers of chatbots.
• How does an LLM work? An LLM (Large Language Model) is an AI model that processes language statistically. It was trained on very large amounts of text and generates answers by predicting sequences of words that fit the context.
• Is GEO the same as SEO? No. SEO primarily targets rankings in classic search engines. GEO aims to become visible in AI answers and to appear as a source or recommendation.
• Which measures matter for GEO? For GEO, clearly structured content that answers user questions precisely and is formulated in a technically consistent way is especially important. In addition, verifiable information, trustworthy sources, and authority signals such as expertise, references, and mentions strengthen visibility in AI answers.

What is GEO?

GEO (Generative Engine Optimization) refers to strategies and measures aimed at making brands, companies, and content visible in the generated answers of AI systems such as ChatGPT, Gemini, Perplexity, or Google AI Overviews.

In contrast to search engine optimization GEO does not focus on the classic ranking in a list of results, but on the direct mention and citation within the AI answer itself.

GEO is a sub-discipline of SEO, because many classic SEO measures also form the necessary basis for optimizing websites in GEO.

Mentions (brand mention) VS. Citations (source citation)

GEO pursues two different goals. Either a brand is mentioned by name in the answer (mention) or content from your own website is cited as a source (citation).

Mentions primarily affect brand perception. Citations position content as a reliable knowledge source for AI systems.

Why GEO

… and not simply keep doing SEO?

SEO remains the foundation for visibility on the web. On its own, however, it is no longer enough. While classic search engines deliver lists of results, AI systems provide directly formulated answers. This is exactly where GEO comes in.

The central difference lies not in the technology, but in the target system and the outcome.

SEO optimizes content for classic search engines. The goal is rankings and clicks on web pages. Optimization is oriented toward keywords, search queries, and factors such as relevance, authority, and technical quality.

GEO is aimed at generative AI systems. The goal is brand mentions and citations within AI answers. Optimization does not target individual keywords, but natural questions, prompts, and content suitable for grounding. What matters is comprehensibility, clear structure, and trustworthy information.

GEO or SEO? In this YouTube video you can learn more about the similarities and differences:

The industry is already responding to this shift. According to a survey by Search Engine Land, GEO is increasingly perceived and used as an independent discipline alongside SEO.

In the context of AI search and generative systems, various terms have become established that describe similar objectives but are focused differently:

• AIO (Artificial Intelligence Optimization)
  An umbrella term for optimizing content and systems for AI applications overall.
• AEO (Answer Engine Optimization)
  Optimization for answer systems with a focus on direct, short answers to specific questions.
• AI-SEO
  A fuzzy term that describes classic SEO approaches with an AI angle, without a clear distinction.
• GAIO (Generative AI Optimization)
  Optimization specifically for generative AI systems, similar to GEO, but less established.
• LLMO (Large Language Model Optimization)
  Focus on adapting content for large language models themselves, independent of the output system.

Regardless of the terminology, one thing holds true: SEO remains the foundation of GEO.

GEO is not a replacement, but a sub-discipline that extends classic search engine optimization with the requirements of generative AI systems.

Learn more about GEO here…

GEO Audit & Strategy Development – Farbentour Updated: May 28, 2026

Farbentour GEO Agency – More Visibility in LLMs Updated: May 28, 2026

What is an LLM?

An LLM (Large Language Model) is a large AI language model that understands, processes, and generates text. Examples include models such as Gemini or GPT-5.2.

Systems such as ChatGPT or Google AI Mode are not models themselves, but so-called Generative Engines. They represent the interface through which users interact with an LLM.

The LLM is the actual AI model in the background. ChatGPT or Google AI Mode make this model usable for people.

The LLM is the engine in the backend. The Generative Engine is the body and the cockpit in the frontend.

How does an LLM work?

1. Prompt (input)
   The process starts with the user's request. The prompt defines what information the AI should deliver.
2. Tokenization (translation)
   The AI breaks the text into small units, so-called tokens. This translates human language into a mathematical format that the model can process.
3. Analysis (understanding)
   The neural network analyzes the meaning and context of the tokens. In this step, the model recognizes whether facts, recommendations, or creative content are being requested.
4. Grounding check (research)
   If the training data is not sufficient, many AI systems fall back on external sources or web searches. Grounding reduces hallucinations and enables source citations. For GEO, this step is decisive, because without grounding no citations or solid brand mentions arise.
5. Integration (context update)
   The content found is loaded into the model's context window. It forms the factual basis for the following answer.
6. Generation (next-token prediction)
   The model generates the answer word by word based on the loaded information. In this step it is decided whether a brand is mentioned or a piece of content is cited as a source.

Excursus: RAG – how it works

RAG stands for Retrieval Augmented Generation and describes a technical method with which AI systems specifically incorporate external information into answer generation.

In simplified terms, RAG runs in three steps:

1. Retrieval
   Relevant content is searched for in a knowledge base, database, or on the web.
2. Add context
   The information found is provided to the model as additional context and loaded into the context window.
3. Generate
   The LLM uses both its internal knowledge and the external content to formulate the final answer.

The central benefit of RAG lies in Grounding. By accessing verifiable sources, fact-based answers with source references are created. At the same time, the risk of hallucinations is significantly reduced.

An LLM without RAG generates answers exclusively from its training knowledge. An LLM with RAG generates answers based on external, up-to-date data.

Different types of Generative Engines

Generative Engines differ in how they obtain information and how answers are generated. For GEO, this distinction is decisive, because not every system handles sources, citations, or brand mentions in the same way.

1. Chat-based Generative Engines
   These systems generate answers primarily based on the trained model knowledge. An external search is not active by default. Examples are ChatGPT without search or Gemini in standard chat mode.
2. Search-based Generative Engines
   Here web search is at the forefront. Answers are strongly based on external sources and are often provided with citations. Typical examples are Perplexity, Google AI Overviews, or Google AI Mode.

1. Hybrid Generative Engines
   These systems combine model knowledge with optional or automatic web search. Depending on the prompt, external sources are included. These include ChatGPT with search activated or Gemini with a search function.
2. Content Generative Engines
   The focus is on creating text or visual content, not on research or source citations. Typical areas of use are marketing and design. Examples are Jasper, Copy.ai, or Ideogram.
3. Agentic Generative Engines
   These systems do not merely respond; they carry out tasks independently, plan steps, and use tools. These include AutoGPT or OpenAI Agents.
4. Domain-specific Generative Engines
   They are specialized in individual fields, such as medicine or law. The answers are based on curated, domain-specific data sources.
5. Multimodal Generative Engines
   Besides text, these systems also process images, audio, or video. Examples are ChatGPT Multimodal or Google Gemini Multimodal.

User behavior & classic search engines in comparison

According to a survey by Bitkom, around half of internet users now use AI chats to answer questions or research information (Source). Especially for complex topics or those requiring explanation, users increasingly turn to AI systems instead of browsing a classic list of results.

Further studies also clearly show this shift. AI chats are no longer used only for general knowledge questions, but also for sensitive areas such as health questions or purchase decisions. Users expect an immediate, comprehensible answer that summarizes and classifies information and provides recommendations for action (Source).

Classic search engines thus do not lose their importance, but change their role. They continue to be used intensively and remain a central point of access to information. At the same time, they increasingly serve as a data and source basis in the background, while the actual interaction takes place via AI systems.

The shift is comparable to media consumption: television, radio, and newspapers exist in parallel and serve different purposes. For companies, this means that visibility no longer arises exclusively through rankings, but increasingly where answers are generated and summarized.

Market share of ChatGPT & Co.

According to estimates, OpenAI processes around 2.63 billion messages per day with ChatGPT (Source Sistrix). This already makes ChatGPT one of the most used digital information systems worldwide today.

What is decisive for SEO and GEO, however, is not pure usage, but search intent. Depending on the calculation model, the share of queries with genuine search or information interest lies between 22 and 50 percent. This means that a considerable portion of interactions is functionally comparable to classic search queries.

From these assumptions, a daily volume of roughly 0.57 to 1.31 billion search-relevant queries results. This means ChatGPT does not yet reach Google's level, but is already moving in an order of magnitude that is strategically relevant for brands and companies.

In parallel, Google Gemini is noticeably catching up. Current usage figures and traffic analyses show that Gemini is gaining in user numbers.

In summary:

• ChatGPT & Co. change the way we access information, and more and more people use it
• Classic search continues to be used intensively by many. The organic traffic that websites received in 2025 has, despite AI search engines, remained almost the same (Source)
• Web & AI: A symbiosis similar to the classic media landscape (TV, radio, newspapers)

How you get found in AI searches

One thing first: there is no single AI system! ChatGPT, Gemini, Google AI Mode, AI Overviews, Perplexity, and other Generative Engines work differently and sometimes deliver diverging results.

Accordingly, it is not enough to align content with a single system. GEO means optimizing across systems.

Prompts

In AI search, individual keywords are no longer the focus, but prompts, meaning naturally formulated questions and instructions.

One principle is decisive here: no citations without grounding.

Only when an AI system accesses external sources do citations arise. Accordingly, only those prompts that can trigger a mention or a citation should be prioritized.

For every prompt, it should be clear in advance which goal is being pursued.

1. Mentions: If your own brand is to be recommended or named in the answer, brand building is in the foreground.
2. Citations: If a piece of content is to serve as a source, you are primarily a knowledge supplier.

Both variants can be valuable, but they pursue different goals and have different effects on leads. Classic SEO tools remain very well suited for identifying suitable prompts. Wh-questions can be found, for example, via Google search:

or analyzed with tools like Sistrix:

Google Search Console also offers, with regex filters, a simple way to identify typical question phrasings, for example:

^(weshalb|welcher|welches|welchen|welchem|was|warum|wann|wo|wie|welche|wer|wieso|wieviel|woran|womit|wodurch|wessen|wovon|worüber|woraus|wohin|woher|ob|kann|dürfen|müssen|habe|hat|haben|seid)\b

It is important, however, not to focus only on wh-questions. Prompts with a comparative or transactional character can also specifically trigger citations, for example with best-of lists or concrete purchase recommendations.

Examples of such prompts are:

• “Show me affordable beach shoes”
• “Name me the best SEO agency for the Düsseldorf area”
• “List the five best mold removers for the bathroom for me”

For GEO, what therefore counts is the targeted selection of prompts with genuine search interest and a clear objective.

For the systematic derivation of suitable prompts, specialized tools such as AlsoAsked are also suitable. They help to cover complete topic clusters and to make connected questions visible that users ask in AI chats.

Query Fan Out

A central concept in this context is the query fan-out. It describes the moment in which an LLM internally splits a single user prompt into multiple search queries in order to gather all relevant information for a comprehensive answer. AI systems such as ChatGPT thereby help indirectly to identify sensible follow-up questions.

A simplified example:

• A user asks: “Find me a family-friendly hotel on Mallorca with slides for under 400 euros per day.”

The AI recognizes several information needs, such as location, target group, amenities, and price. From this, several sub-queries arise in the background, for example:

• “Best family hotels Mallorca 2025”
• “Hotels Mallorca water slides rating”
• “Hotel prices Mallorca July average”
• “Safety children's pools Mallorca hotels”

For GEO this is decisive. There is no longer a single main keyword. The AI does not search for one term, but for answers to sub-aspects.

The area of coverage only increases when content concretely answers these sub-queries. Anyone who does not cover relevant follow-up questions is selected less often as a source in the grounding process.

Prompt ideas can still be developed and analyzed very well with classic SEO tools such as Sistrix or Ahrefs.

What is decisive, however, is the evaluation behind it: We optimize for prompts with clear search intent that can trigger either a mention or a citation. This search intent must be hit.

Content for LLMs

Optimizing content for GEO differs only marginally at its basis from classic SEO, but it shifts the focus: while SEO optimizes primarily for the human reader, GEO content must additionally be extractable for the machine.

AI systems do not read texts the way humans do, from top to bottom. They scan for patterns, entities, and connections. Successful content must therefore be prepared in such a way that crawlers can isolate relevant information (“content chunks”) and reassemble it.

Structure and “content chunks”

AI models prefer content that is built up modularly. Long walls of text without interruption make it harder for the algorithm to extract the sought answer precisely.

• Clear hierarchies: Subheadings (H2, H3) act as anchor points. Ideally, they should be formulated so that they could themselves serve as a search query or prompt (e.g., “How high are the costs?” instead of “Prices”).
• Lists and tables: For comparisons, process steps, or data overviews, structured formats such as bullet points or HTML tables are important. AI systems can process these formats significantly better than running text and prefer to use them for direct answer generation.

Information density and factual language

A decisive factor for being taken up into AI answers is information density. Models such as GPT or Gemini filter out “noise.”

• Factual language: Unnecessary filler words and purely promotional adjectives should be avoided. The writing style must be precise, logical, and informative.
• Avoiding thin content: Content without substance confuses not only classic search engines but also AI crawlers. It offers no added value for training or grounding the model and is ignored.

Content freshness and topicality

Especially with search-based Generative Engines (such as Perplexity or Google AI Overviews) that use RAG technology, topicality is a hard ranking criterion. For topics that develop dynamically, the systems prefer the most recent reliable source.

Outdated information increases the risk of hallucinations (false statements by the AI) and is therefore often sorted out in the selection process.

Content strategy: From general knowledge to expert insight

Content optimization for GEO follows a twofold logic: we want to force Citations (for traffic) and secure Mentions (for branding).

Traffic primarily arises where we close a knowledge gap (data gap). As long as an AI can answer a question from its general training knowledge, it will not link to an external source. To obtain a citation, content must be so current or specific that it practically forces the model to look it up.

The difference lies in specificity:

• A generic article (“What is a PV system?”) offers no Information Gain. The model already knows the answer and will ignore the source.
• A “price comparison of PV storage systems 2026,” on the other hand, delivers exclusive data that the model cannot know. To answer the question correctly, the AI must call up and cite the source.
• The same principle applies to complex advisory situations. Users often ask AIs very granular questions, such as: “Which desktop PC under €1,500 is best suited for 4K resolutions?” Content must cover exactly these scenarios. Anyone who only delivers general hardware tips here falls through the cracks.

The situation is different with Mentions (brand mentions). For direct recommendation questions (“Which SEO agency is recommendable?”), what counts is not the novelty of the information, but the brand authority in the training data. Here the strategic goal is for the AI to store your own brand as the de facto standard and to name it as the first recommendation.

Query fan-out: Anticipating the follow-up question

When AI models process a complex request, they internally break it down into several sub-questions (query fan-out). A GEO strategy must anticipate these “implicit questions.” Anyone who writes about a topic must also cover the adjacent aspects:

• “What happens if…?”
• “How does X differ from Y?” Only those who cover the entire topic field are drawn upon as an authoritative source for the synthesized answer.

Problem-solving instead of theory

Users increasingly use AI systems as personal assistants. They are not looking for definitions, but for solutions.

Generic glossary articles and standard definitions offer no added value here and are ignored by the algorithms. What counts in the AI era is genuine expertise. Deep subject knowledge and validated human experience are the only factors that an AI cannot simulate.

Content must therefore move away from theory and toward concrete problem-solving (guides, case studies, recommendations for action).

The information architecture: The “inverted pyramid” system

So that content is not only read by an AI, but recognized as the answer, the structure of how information is conveyed must be simplified.

Here the “principle of the inverted pyramid,” known from journalism, has proven useful for GEO.

LLMs look for the definition and the core answer in the most prominent place.

• The answer first: The most important conclusion or the direct answer to the prompt belongs at the beginning of the paragraph.
• Simple language: Complex nested sentences make parsing harder. A clear subject-predicate-object structure increases the likelihood of a correct extraction.
• Logical deepening: Only after the core answer do details, background information, and nuances follow.

Anyone who structures their content this way delivers the AI the perfect “building kit” for answer generation.

Content chunks and FAQs

FAQs are ideally suited for GEO. Since AI models break complex requests down into sub-aspects (query fan-out), FAQs deliver the matching, granular answers (“content chunks”). Each question-answer combination is a self-contained data package that can be extracted by the AI.

The central success factor for GEO can be reduced to a single sentence:

Content must be so specific and high-quality that AI engines must reference it as a primary source.

As long as a model can answer a question from its general training knowledge, it will not link to an external source. Only when content closes an information gap does the necessity of a citation arise.

Warning against pure AI content

Using AI exclusively to create text usually prevents visibility in AI answers. Since language models are based on probabilities, they generate the average of all training data. Anyone who publishes AI texts offers no added value and does not become a thought leader. Only content with human expertise, real data, and a clear stance receives mentions and citations.

Technology: Indexability as the foundation

What the crawler cannot read does not exist for the model. Since there is (as yet) no “ChatGPT Search Console,” direct feedback about indexing problems is missing.

• Full focus on indexability
  Classic SEO hygiene is indispensable for GEO. Title tags, meta descriptions, and schema data provide the AIs with the necessary context to classify content correctly. Regular checks with crawling tools are mandatory to ensure that bots can capture the content.
• Do not hide content behind JavaScript
  LLM crawlers (such as the GPTBot) often render websites worse than the Googlebot. Content must be present as plain text in the HTML source code. Websites that rely heavily on client-side rendering (“heavy JavaScript”) risk the AI seeing an empty page.
• Crawl budget, load times, and hygiene
  Load times are critical: if the server responds too slowly, the crawler aborts. While training bots archive the web without time pressure for future model updates, real-time user bots must deliver immediately on live requests and skip a page if the server takes longer (approx. 500ms) to respond. In GEO practice, server performance is therefore no longer an optional bonus, but the hard entry ticket for every live citation. In addition, it is important to minimize the “noise.” A “spring cleaning” (content pruning) helps: outdated or irrelevant content should be deleted or set to noindex. This directs the bots' limited resources to the truly relevant content.
• Robots.txt
  The most common mistake is accidentally locking out the future. Anyone who blocks crawlers such as GPTBot, CCBot (Common Crawl), or Google-Extended in the robots.txt actively decides against a presence in AI answers. URL parameters and constantly changing URLs should also be avoided in order to offer the bots stable paths.

Common Crawl and JavaScript

Many LLMs train their models with open source crawlers, such as the Common Crawl. These crawlers are technically far less powerful than Google's crawlers. Many of these open source crawlers often do not fully execute JavaScript and fail with complex client-side rendering architectures.

Anyone who hides too much content behind JavaScript is still visible to Google, but often invisible for LLM training. Technical optimization must therefore focus again on the absolute basics:

• HTML first: Relevant content must be in the source code.
• Freedom from errors: Technical hurdles (404 errors, redirect chains) weigh more heavily here. The use of professional audit tools such as Screaming Frog or Seobility to regularly check indexability is mandatory.

Test: How does the crawler see your website?

Since there is no official preview for LLM crawlers, the Internet Archive (Wayback Machine) serves as the best proxy. Since the Web Archive and Common Crawl use similar technologies and data sources, a look into the “Wayback Machine” shows what was actually captured.

If images, texts, or entire navigation elements are missing there, it can be assumed that these are also missing from the AI's training data.

Example check: A look at the snapshot of the Farbentour contact page shows whether all information was readable for the crawler.

Robots.txt and Cloudflare

Out of fear of AI, many administrators block all AI bots across the board via the robots.txt. Anyone who blocks crawlers such as GPTBot, CCBot, or anthropic-ai consciously decides against a presence in AI search.

Security providers such as Cloudflare offer functions like “Block AI Scrapers.” These are often set by default or very aggressively. For a GEO strategy, these firewalls must be configured so that they let legitimate AI crawlers pass.

Warning: A robots.txt that blocks everything is the end of every GEO effort. Such a configuration ensures that brands are deleted from the AI's digital memory. Here is a negative example of a block: https://github.com/ai-robots-txt/ai.robots.txt/blob/main/robots.txt

LLM text file

The llms.txt is a proposed standard for websites (see llmstxt.org). Like the robots.txt, this file is placed in the root directory. It is intended to provide AI crawlers with targeted links to markdown-optimized text files in order to enable reading content without HTML ballast.

At present, the implementation has no measurable influence on GEO. Analyses show that the relevant large language models and Generative Engines currently ignore this standard (Source). There is no empirical evidence of improved visibility or increased citation frequency. Implementation is not necessary at this point in time.

Log file analysis: Identifying hallucinated URLs

An underestimated problem with AI bots is hallucinated URLs. Since LLMs are based on probabilities, they occasionally “invent” URL paths that sound logical but do not exist. If the bot then tries to call them up, unnecessary 404 errors arise that burden the crawl budget.

Regular analysis of the server log files (e.g., with the Screaming Frog Log File Analyser). If accumulated 404 accesses by AI user agents to unknown URLs are found, these should be checked and, if necessary, redirected via a 301 redirect to the correct content.

HTTP 499:

In the log files, pay special attention to the HTTP status code 499, as this is a direct warning signal for aborts caused by response times that are too slow. Anyone who discovers this error with user agents such as ChatGPT-User immediately knows that the AI has lost patience and that your website was technically sorted out before it could even be read. You can find more on this topic here.

Load times & Core Web Vitals

While the Googlebot has massive resources and occasionally forgives minor latencies, many AI crawlers and the Common Crawl work with considerably more limited budgets.

If a website does not load fast enough, the crawler can abort the process. The content is not read and indexed in the first place. The focus must therefore not lie only on general load time. Passing the Core Web Vitals (LCP, INP, CLS) is important. An unstable or slow page signals low quality to the bot – and in the worst case leads to the direct abortion of crawling.

Structured data

Structured data (via JSON-LD) translate “content” into machine-readable facts.

With schema we dictate the interpretation: “This is not random text, this is a guide,” “This is an author.” This semantic markup massively increases the likelihood that information is extracted correctly and assigned to the correct entity.

Analyses (see Sistrix: The Path to the AI Citation) show: AI models prefer to read authority signals directly from the source code (e.g., @type: Organization) instead of laboriously reconstructing them from the running text.

To be understood as an entity, the following types are mandatory:

• Organization: Legitimizes the company as a brand.
• Person / Author: Links content with experts (E-E-A-T).
• Article / NewsArticle: Helps with temporal classification (topicality).
• Product / Offer: Delivers hard facts for transactional prompts.
• FAQPage: Delivers perfect “content chunks” for direct answers.
• LocalBusiness: Anchors the brand locally.

For error-free creation and validation of the JSON-LD code, the Schema Markup Generator by TechnicalSEO is recommended.

Practical tip: Would you like to check your website's GEO optimization potential? Then take a look at our YouTube video on the topic of “GEO audit”:

Entity and E-E-A-T

An entity is a uniquely identifiable object (a brand, a person, a product) that the AI understands and can assign within its semantic context.

“In AI search, it is no longer about ranking for terms, but about your brand existing as a unique, trustworthy entity (an ‘object’ with clear attributes) in the AI's knowledge network.”

To achieve this, three factors are decisive:

1. Contextual relevance: AI models such as ChatGPT, Gemini & Co. understand semantic connections. Content must consistently link the brand with specific subject topics. Anyone who writes about SEO today and about ornamental fish tomorrow dilutes their entity profile.
2. Digital footprint: Visibility arises through external validation. An entity only becomes relevant for the AI when it is consistently mentioned on authoritative third-party platforms such as Wikipedia, LinkedIn, Trustpilot, or specialist portals.
3. Citability: To become the primary source, the website must serve as a “fact node.” This only succeeds by providing unique data or in-depth analyses that the model can rely on.

E-E-A-T in the AI era

The E-E-A-T concept (Experience, Expertise, Authoritativeness, Trustworthiness) is known to every SEO. Since LLMs are trained to minimize false information, they prefer sources with high trust value.

• Experience: AI cannot experience anything. This is precisely where the advantage of human authors lies. AI models prefer “real content from real people.” Personal insights, anecdotes, and real case studies are signals that an AI cannot simulate.
• Expertise: Generic, run-of-the-mill glossary texts are worthless. They must be replaced by in-depth expert knowledge. The AI recognizes information density and subject-matter depth. Superficial content is ignored, deep dives are referenced.
• Authoritativeness: Those who are cited win. Digital PR and guest articles in specialist media signal to the AI that your own content is worth referencing. The more often an entity is named in established sources, the higher its weight in the model.
• Trustworthiness: Claims must be verifiable. Naming sources and providing hard data (prices, technical facts) reduces the risk of AI hallucinations. Content that is verifiable massively increases your own citation rate.

Practical example for entity and EEAT

How does a brand become an authority for the AI? This is best clarified using three central questions that an LLM must answer in order to classify an entity correctly.

1. The “Who are you?” question (identity & entity)

In AI search, it is no longer about ranking for individual keywords. The goal is more strategic: the AI must store your own brand as the one expert entity.

This does not happen on your own website alone, but through links and signals from external platforms. A brand must be present where the training data is derived, such as:

• Reddit & communities
• Gamer forums & specialist portals
• YouTube (own channels & external mentions)
• Social media

Example: The PC manufacturer Dubaro shows exemplarily how entity building can work. The brand did not rely solely on SEO, but built up a massive presence in the community. Through targeted collaborations with influencers (e.g., HardwareDealz) and high visibility on YouTube & Co., the brand name is permanently mentioned in the context of “gaming PCs.”

Through these thousandfold mentions and signals, the AI learns: “Dubaro belongs to the semantic core of gaming PCs.” Anyone who asks an AI for PC recommendations today can hardly get past this entity.

1. The “What can you do?” question (Experience & Expertise)

To be cited as a source, theoretical knowledge is not enough.

• Experience & Expertise: Generic “What is…” texts must be replaced by real tests, unboxings, and opinions.
• Authoritativeness: Authority arises when third parties report on the expertise. Backlinks and mentions from specialist portals signal to the AI: this content is worth referencing.

1. The “Why you?” question (Trustworthiness & transparency)

LLMs prefer sources that are transparent and verifiable.

• Create transparency: Instead of copying anonymous product descriptions, your own product tests with real author names must be published.
• Make the company tangible: An “About us” page must not be filler text. It serves as evidence of the existence of real people and expertise.

Example: An example of trust-building measures is provided by Liebscher & Bracht. Through extremely detailed “About us” sections, the presentation of the team, and explicit quality promises, the abstract company name becomes a tangible, trustworthy organization. (See:The Team andQuality promise)

Backlinks & Mentions: From the link to the mention

In classic SEO, the backlink is the primary ranking factor. In GEO, the focus shifts: LLMs do not “click” their way through the web, they process information. Backlinks function here less as a path and primarily as a trust element. What is decisive are the anchor texts: A link from a specialist portal signals to the model that the linked content is authoritative and worth citing. The link is the proof of relevance.

The power of mentions

Even more important than the technical link, however, is the mere mention. The likelihood that an AI recommends a brand increases with the frequency of its mention in relevant contexts. The more often a brand is named as the solution to a problem (even without a link), the more strongly the neural network links the entity with the topic.

Building “top lists” (listicles)

Data from Ahrefs (source: Best Lists Research) show that structured lists (“The best tools for…,” “Comparison 2026”) are the most important lever for GEO.

• Dominance: Listicles are, at 43.8% the most common type of source that ChatGPT uses for recommendations.
• Positioning: There is a clear correlation – the higher a brand is placed in a list, the higher the visibility in the AI answers.
• Topicality pressure: AI engines prefer “fresh” content. More than 79% of the cited lists were updated in the current year. Historical authority is beaten by current relevance.

Warning: Quality before spam

The hunt for list placements carries risks. Google now acts aggressively against “parasite SEO” and low-quality “best-of” lists on off-topic portals (see Lily Ray: Google Crackdown). What looks like cheap spam is penalized.

Listings are only valuable if they come from thematically fitting, extremely strong websites and appear organic. Anything that looks like a “bought fake review” harms the entity more in the long run than it helps.

YouTube: Relevance vs. spurious correlation

The following Ahrefs study shows a strong connection between YouTube presence and visibility in AI answers. Brands that are strongly represented on YouTube are recommended more often by AIs.

Important: Caution is advised here regarding spurious correlations. It is not guaranteed that the video itself is the cause. It is more likely that strong brands are generally present on all channels (text & video). Nevertheless: video content feeds the entity with context. Anyone who is present on YouTube increases the likelihood of being anchored in the AI's “world knowledge.”

The dominance of comparison lists

The biggest lever for GEO, according to the data, is, however, text-based lists.

• Dominant format: 43.8% of the sources that ChatGPT uses for recommendations are comparison lists (“The best X for Y”).
• Visibility: Without a presence in such lists, a brand effectively does not exist in the AI's recommendation logic.
• Ranking factor: The position within the list is decisive. The further up a brand appears in an external list, the more likely it is to be adopted by the AI as a top recommendation.

Topicality pressure (freshness)

Historical authority loses value. AI models prefer extremely current data.

• 79% of the cited sources come from the current year.
• Anyone who has outdated lists or is listed in old articles is ignored.

Opportunity for niche players: Your own lists & updates

The data shows a massive opportunity for smaller websites: Relevance beats backlink strength.

• Content before power: 35% of the cited sources have a low domain authority. You don't have to be an industry giant to be cited – you just have to have the most current list.
• Your own lists: Self-published best-of lists (“Our top products”) also promote AI citations, as long as they are factually correct and structured.
• Update culture: The key to a lasting status as an AI source is frequency. A monthly update of data and lists signals constant relevance to the crawler.

Strategy for more mentions: Reverse engineering

How do you find out on which platforms you need to be present? The answer is banal: you ask the AI itself. Instead of building links blindly, we use reverse engineering to identify the exact sources that the AI trusts.

The process:

1. The prompt: Ask ChatGPT, Gemini, or Perplexity directly: “Name me the 5 best providers for [your service / product].”
2. The follow-up: Once the list is generated, you follow up: “Why did you select these? On which sources is this recommendation based?”

The insight: The AI will disclose its “grounding sources.” Often these are not the biggest news sites, but very specific niche portals, forums (like Reddit), or review platforms (like OMR Reviews or Gelbe Seiten).

The recommended action: Once the source is identified, the obligation is: You must be listed there. If, for the query “Best SEO agency,” ChatGPT primarily cites OMR Reviews as evidence, an entry in a general business directory is of no use. Visibility in the AI answer depends 100% on the presence on this one, specific platform.

GEO manipulation & prompt injection

Naturally, many try to outsmart new systems. One method is prompt injection: Here the attempt is made to steer the AI through manipulatively placed commands within data or website content.

An example from Cameron Mattis shows how easily AIs can be influenced: he integrated an instruction into his LinkedIn profile stating that LLMs reading his profile for headhunters should not output job analyses, but recipes for flan. The AI bots actually followed the instructions in his profile instead of their original task.

The “GTA 6 twerk button” debacle

That AIs are manipulable is shown by the case of the “GTA 6 twerk button.” Here Google's AI Overview blindly adopted a satirical Reddit post as fact and claimed that the game contained this feature (source: GamePro).

The example proves: the systems still have difficulties recognizing irony or targeted misinformation (data poisoning).

Why manipulation is not a strategy

But the comparison to “SEO 2006” is flawed. Back then, spam worked for years. Today, Google and OpenAI often patch such gaps within hours.

• No sustainability: A hack may work for a day, after which the gap is closed or the domain is penalized.
• Constant validation: Through RAG and fact-checking algorithms the plausibility of sources is permanently reassessed.
• Difficult to implement: Targeted manipulation requires technical understanding of the model architecture and is hardly scalable.

Anyone who relies on manipulation gains attention in the short term but risks the complete loss of the entity's trust in the long run.

Measuring success and monitoring

Anyone who measures GEO by the standards of classic SEO or performance marketing will be disappointed. Click-through rates (CTR) and visitor volume are significantly reduced with AI answers. The user receives the answer directly in the chat and clicks on the source less often. GEO is not a volume channel, but leans strongly toward being a branding channel.

The new KPIs: Mentions & Citations

Success is not measured in sessions, but in the “Share of Model”:

• How often am I recommended as a brand?
• In how many answers do I appear as an expert?
• Am I cited as a primary source?

These Mentions (brand mentions) and Citations (source references) are the hard currency. They must be captured quantitatively.

Tracking setup & tools

For monitoring these new KPIs, a mix of classic SEO tools and manual analysis is needed:

1. AI Overviews & Google: Here tools such as Sistrix or Ahrefs already deliver very good data on visibility in AI Overviews and the AI Mode.
2. Traffic analysis: In Google Analytics 4 (GA4) referrers from AI systems (e.g., chatgpt.com, bing.com, perplexity.ai) should be segmented separately. A Looker Studio Dashboard helps make these often small but highly converting streams visible.
3. Monitor your own prompts: For strategically important search queries (“Best agency for X”), manual monitoring or the use of specialized scripts is recommended to check positioning in the answers.

Warning: The “tool blind spot”

Be careful with automated tracking solutions for ChatGPT & Co. Many providers crawl prompts via free, non-logged-in API interfaces (mostly GPT-4o “clean slate”). The reality at the customer looks different:

• Real users often use ChatGPT Plus, Claude or Perplexity Pro.
• They have a chat history (memory) that personalizes the results.

This creates massive deviations between what the tool reports and what the potential customer actually sees. Tracking data in GEO is therefore always only an indicator, not an absolute truth.

Practical case: Online shop for shipping boxes

That GEO and SEO are not separate silos is shown by our current case from the B2B area of “shipping boxes.”

The goal was market leadership in both worlds: maximum visibility in classic Google search and establishment as the standard recommendation in AI chats.

The results: Dominance in search & chat

The numbers prove a direct correlation between classic optimization and AI visibility:

• GEO growth: The mentions in AI systems rose from 600 to over 5,194. The shop is today recommended in numerous prompts as the leading online shop for packaging material.
• SEO power: In parallel, TOP 3 rankings were achieved for the toughest money keywords (“Kartons,” “Versandkarton,” “Maxibriefkarton”). Strong competition was pushed aside.

The measures: What we did

The success is based on a strategy that combines technical excellence with targeted entity building.

Technical foundation & UX (crawlability)

• Relaunch & structure: The website was completely cleaned up. A modern design and intuitively guided navigation ensure that users (and bots) find their way around immediately.
• Speed & code: Load times (Core Web Vitals) were massively improved to prevent crawler aborts. Comprehensive Schema.org data now make products machine-readable.

Content with information gain

• No “AI blah-blah”: Building an expert section with in-depth content, written by humans.
• Content chunks & basics: Integrating buying guides and FAQs into all categories delivers the AIs the perfect morsels for direct answers. At the same time, all SEO basics (title tags, meta descriptions) were perfected.
• New clusters: Creating specific categories (e.g., “Amazon shipping boxes”) that serve exact search intents and prompts.

Authority & GEO levers (off-page)

• Trust signals: Organic building of reviews on platforms such as Trustpilot. AIs use these as validation for “best shop” queries.
• Digital PR & lists: In addition to classic link building, the focus was on GEO: we identified which sources are used by ChatGPT & Co. and specifically secured placements in top lists there.
• Iterative optimization: The success is not a product of chance, but the result of constant testing and adjustments of meta data and content.

This case proves: anyone who does their homework in SEO (technology, content, trust) and extends it with GEO specifics (top lists, entity data) becomes visible in both channels.

You can learn even more about Generative Engine Optimization in our YouTube video:

Summary

GEO is not an isolated channel, but a new discipline of modern search. Anyone who wants to win here must decide strategically: what is the goal?

1. The strategy: Mention or citation?

Goal: obtain mentions: You want the AI to know and recommend your brand. Build reputation on the web. Ensure mentions on external sites, appear in “top lists,” maintain a strong “About us” page, and collect aggressive social proof (e.g., reviews on Trustpilot).

Goal: obtain citations: You want the AI to use your content as evidence. Become the primary source. Create your own top lists, studies, surveys, and unique data that force the model to “look things up” (information gain).

2. The feedback loop: Ask the AI

Your best GEO consultant is the AI itself. Use reverse engineering:

“Why don't you name my brand or cite me as a source?”

“Why do you mention or cite competitor XYZ more often?”

“On which data are your recommendations based?” The AI's answers guide you to the missing sources and topics.

3. GEO vs. SEO: The subtle difference

In classic SEO you concentrate on keywords. No matter what is searched for – as long as it is part of the customer journey, you optimize for it in order to capture traffic and leads.

In GEO, class counts, not mass. The focus is on master prompts that trigger a “grounding” (backing up with facts). You don't have to rank for everything, but only where the AI absolutely needs an external source.

4. The basis: No GEO without SEO

Don't be blinded by the hype: SEO basics are the absolute prerequisite for GEO. If the technology isn't right, the content is invisible to the AI.

• Clean title tags & meta descriptions
• Avoiding duplicate content
• Fast load times (Core Web Vitals)
• Logical text structure (H-tags)
• Highly informative content

Without this foundation, every AI strategy comes to nothing.