(Living Article – continuously updated)
Artificial intelligence has completely transformed digital marketing in a very short time. New terms are emerging, old ones are being redefined, and suddenly half the team is discussing AEO, GEO, and LLMO as if it had always been that way.
This glossary consolidates the most important AI terms that will have a massive impact on brand visibility, search, and content strategies in 2026.
Contents
Why is this glossary needed?
Marketing is facing a new core question: How does a brand make it into the answers of AI systems?
Search behaviour is shifting rapidly, from traditional SERPs to generative engines such as ChatGPT, Gemini, Copilot, and Google AI Overviews. This does not mean that Google is becoming irrelevant – but it does mean that an additional, growing channel is emerging that brands should actively leverage. Anyone who wants to be found, mentioned, and cited needs a clear understanding of the new disciplines. The four central pillars of visibility in AI systems (AI Visibility) are called: AIO, AEO, GEO, and LLMO. This glossary explains them – and goes beyond.
The four pillars of AI visibility at a glance
The following table shows at a glance how the four disciplines differ:
| Discipline |
Core Question | Where it operates | Goal |
| AIO – AI Overviews | “Am I shown in AI Overviews?” | Google AI Overviews | Presence in Google’s AI summaries |
| AEO –Answer Engine Optimisation | “Am I the answer?” | Featured Snippets, People Also Ask, voice search, AI Overviews | Direct answer in search engines and AI systems |
| GEO – Generative Engine Optimisation | “Do I appear in AI answers?” | ChatGPT, Perplexity, Copilot, Gemini | Brand framing in generative answers |
| LLMO – Large Language Model Optimisation | “Am I a reliable source of knowledge?” | ChatGPT, Claude, Gemini, RAG-Systems | Become part of the knowledge base of language models |
How do the four pillars relate to each other?
The disciplines are not isolated silos – they interlock. AIO is essentially a subset of AEO, but focuses specifically on Google’s AI summaries. GEO and LLMO share the same sphere of impact (generative AI systems), but differ in leverage: GEO optimises how a brand is represented in answers; LLMO ensures that it is anchored in the model’s knowledge base in the first place.
Put simply: If you do LLMO well, you lay the foundation – but without GEO, you lack control over the framing. And anyone who only engages in AEO misses the growing audience that no longer starts with Google at all.
Where to start? For most brands, AEO is the fastest entry point because it builds on existing SEO structures. GEO and LLMO require a broader content and PR strategy – but are particularly worthwhile for brands in consultancy-intensive industries.
AIO – AI Overviews
AI Overviews are Google’s AI-generated summaries directly in the search results. They compile information from various sources – mostly authoritative, entity-driven, and reputation-based.
Practical example: A search query such as “best CRM software for SMEs” delivers a summarised answer in AI Overviews with three to five recommendations. To appear there, you need not only good content – but also consistent mentions on comparison portals, in trade media, and on review platforms.
AEO – Answer Engine Optimisation
“Am I the answer?” AEO places exactly one question at the centre: How does a brand become the direct answer in AI systems? It is about structuring and preparing content in such a way that search engines and AI systems deliver it directly as the solution and not merely, as in classic SEO, as one of ten results.
Where AEO operates:
- Google Featured Snippets
- Google “People Also Ask”
- Voice search (Assistant, Siri, Alexa)
- AI Overviews
Typical measures:
- Precise question-and-answer formulations directly in the content
- Directly formulated benefit in the first sentences
- FAQ and HowTo markup (structured data)
- High topical authority through depth instead of breadth
Practical example: A SaaS provider optimises its pricing page with a clear question-and-answer structure: “How much does product cost per month?” – followed by a precise answer in one to two sentences. Result: The page is displayed as a Featured Snippet and read aloud by Alexa in voice searches.
GEO – Generative Engine Optimisation
With GEO begins what many marketers are only just discovering: optimisation for how AI talks about brands – independently of Google.
Generative engines such as ChatGPT, Perplexity, and Copilot completely reformulate answers and rely on preferred sources in doing so. GEO influences which statements these systems adopt and how they present them. The technical term for this is brand framing – the way in which a brand is contextualised, positioned, and described in AI-generated answers.
Focus of GEO:
- Brand framing in AI answers
- Mentions and citations in generative results
- Consistency across various sources
- Strengthening of entities and context
The difference between SEO & GEO in a nutshell:
| SEO | GEO | |
| Ziel | Click on an organic search result | Mention or citation in an AI answer |
| Optimised For | Google, Bing & Co. | ChatGPT, Perplexity, AI Overviews, Copilot |
| Content Style | Keyword-focused, click-driven | Citable, fact-based, context-rich |
| Results | Blue links in SERPs | Mention in generative answers |
Practical example: An agency finds that ChatGPT, when asked “Which online marketing agencies specialise in international SEO?” only mentions competitors. The GEO strategy: targeted expert articles on industry portals, guest contributions with clear positioning, and consistent mentions in interviews. After a few months, the agency appears in the AI answers – including correct framing.
What distinguishes GEO from LLMO in practice?
The question arises frequently – and rightly so. Both disciplines concern AI systems such as ChatGPT or Gemini, but the point of approach differs:
- GEO optimises the representation: How does the AI talk about the brand? Which framing, which tonality, which facts are adopted?
- LLMO optimises the anchoring: Is the brand present in the model’s knowledge base at all – and classified as a reliable source?
Put simply: LLMO ensures that the AI knows a brand. GEO ensures that it describes it correctly.
LLMO – Large Language Model Optimisation
LLMO operates at a deeper level than GEO: How does a brand become part of a language model’s knowledge?
Here the focus is less on search results and more on the knowledge base and model understanding. While AEO and GEO optimise the output, LLMO targets the input – that is, the data from which models learn and which RAG systems retrieve in real time.
Important for:
- ChatGPT
- Claude
- Gemini
- RAG systems in companies (see chapter below)
Typical measures:
- Clear, fact-based content with verifiable statements and source references
- Strong entities and consistent terminology across all channels
- Clean, crawlable page structure – so that AI crawlers can capture the content
- Regular content updates in order to remain current in the training cycle
Practical example: A B2B software provider maintains an extensive, publicly accessible knowledge database with clearly structured product descriptions, comparison tables, and technical specifications. This content is classified by LLMs as a reliable source – with the result that ChatGPT directly refers to this documentation in product-related questions.
The difference to AEO in implementation: AEO structures content for search engine answers (Featured Snippets, People Also Ask). LLMO structures content for model training and RAG retrieval – meaning: less focus on individual keywords, more focus on complete, context-rich blocks of information.
Looking for buzzwords? – AI terms from A to Z
Use our AI directory to jump directly to the entry of your choice.
AI terms from A to Z
→ Citation
→ Digital PR for AI Visibility
→ Earned Media vs. Owned Content
→ Entity
→ RAG – Retrieval-Augmented Generation
→ Structured Data / Schema Markup
→ Token
AI Brand Monitoring
AI Brand Monitoring analyses how and how often a brand is mentioned in AI answers – including tonality, facts, and sources. In 2026, this is a mandatory field, as AI is increasingly used as a decision assistant.
Practical example: An e-commerce company reviews monthly how ChatGPT and Perplexity respond to the question “Which online shop has the best customer service?” This makes it possible to detect shifts in AI framing at an early stage – and counteract them in a targeted manner.
AI Reading
AI Reading describes the ability of tools such as ChatGPT or Claude to make visible the sources they have used for an answer. This finally creates transparency about where an AI obtains its information.
Practical example: A user asks Perplexity about the best project management tools. The answer shows five recommendations – and below them the linked sources. Anyone appearing there as a source gains visibility and trust.
AI Share of Voice
AI Share of Voice measures how large a brand’s share is in AI answers – compared to competitors. If ChatGPT names five names in response to the question “Which SEO agencies are recommended?”, each of them has a share of voice of 20 per cent.
Practical example: A company tracks monthly how often it appears in Perplexity answers compared to three main competitors. The result shows: Competitor A dominates – because it publishes significantly more expert articles on cited portals. The reaction: targeted development of earned media on the same platforms.
AI Visibility
AI Visibility describes how present and how correctly a brand is represented in the answers of AI systems – across all generative engines, AI Overviews, and LLM-based tools. It is not only about whether a brand is mentioned, but also how: with which framing, which tonality, and which facts.
Why this matters: A brand can rank in first place in classic SERPs and still be invisible in AI answers – or be represented incorrectly. AI Visibility makes this blind spot visible.
Citation
The visible link or source reference that AI tools display in their answers. For brands, this is a completely new visibility channel. The most important metrics:
- Mention = reference without a link
- Direct Citation = reference with a direct link to one’s own website
- Indirect Citation = reference via a third-party site that refers to the brand
- Sentiment = tonality of the brand mention (positive/neutral/negative)
Content Authority
Content Authority describes the topical authority of a source – that is, how reliable and competent an AI system assesses a website or an author on a specific topic. It does not arise from a single article, but from consistent, in-depth engagement with a topic over time. Anyone who publishes ten superficial articles on a topic loses against someone who writes three truly good ones.
Digital PR for AI Visibility
Digital PR for AI Visibility is targeted press work with the goal of appearing in the sources that AI systems prefer to cite – trade media, industry portals, review platforms, and news aggregators.
The difference to classic digital PR: Classic digital PR optimises for backlinks and traffic. Digital PR for AI Visibility optimises for citability – meaning that AI systems classify the content as a reliable source and include it in their answers.
Discovery vs. Fact-Finding
Not every search query is the same – and AI systems treat them differently:
| Query Type | User Intention | Preferred Sources |
| Discovery | Orientation, comparison, understanding | Earned Media, reputation-strong sources |
| Fact-Finding | Finding concrete details | Owned Content – Documentation, Guidelines, Productdetails |
What this means in practice: For discovery queries, presence in independent sources counts – trade magazines, comparison portals, industry directories. For fact-finding queries, the quality of one’s own content counts. A good AI visibility strategy covers both.
Earned Media vs. Owned Content
Two terms that take on new meaning in the AI context:
Owned Content: Content on one’s own channels – website, blog, documentation, product pages. Particularly relevant for fact-finding queries.
Earned Media: Mentions by third parties – trade media, review portals, guest contributions, interviews. Particularly relevant for discovery queries, because AI systems prefer independent sources.
Entity
An entity is a clearly defined concept that AI systems recognise as an independent unit – a brand, a person, a product, a place. Google uses the Knowledge Graph to connect entities; LLMs work with similar concepts.
Why this is important: The more clearly a brand is defined as an entity (consistent name, unambiguous description, structured data), the more reliably it is recognised and correctly assigned by AI systems.
Hallucination
An AI model generates an answer that is factually incorrect but sounds convincing. For brands, this is a risk: Incorrect prices, outdated product information, or invented statements can appear in AI answers – and are often not questioned by users.
Countermeasure: Clear, up-to-date owned content with verifiable facts reduces the risk of hallucination, because AI systems can rely on reliable sources.
Grounding
Grounding refers to the process by which an AI model bases its answers on verifiable, external sources – instead of relying exclusively on training data. Google Gemini, for example, uses grounding to compare answers with current web content.
For you, this briefly means: The more suitable your content is as a grounding source (current, fact-based, well structured), the more likely it is to be used by AI systems.
Multi-Source Consistency
Multi-Source Consistency describes the consistency of brand information across all public sources – website, social media, industry directories, press reports, Wikipedia. AI systems compare information from different sources; contradictory information leads to uncertain or incorrect representations.
Practical example: A company calls itself “Peak Ace” on its own website, “Peak Ace AG” on LinkedIn, and “PeakAce” in an old press report. For an LLM, these are potentially three different entities – with the risk that information is assigned incorrectly or not displayed at all.
Prompts and Prompt Research
Prompts are no longer just a tool – they are a genuine research instrument. Prompt Research means systematically testing:
- How AI talks about an industry
- Which sources are preferred
- Which frames (recurring representation patterns) and narratives (overarching storylines) appear in AI answers
Practical example: A team enters the same question in ChatGPT, Gemini, and Perplexity – “What trends are there in performance marketing in 2026?” – and compares the answers. Differences in sources, tonality, and brand mentions show where one’s own AI Visibility is strong and where gaps exist.
RAG – Retrieval-Augmented Generation
RAG is a method in which a language model not only relies on its training knowledge, but retrieves external sources in real time and incorporates them into the answer. Perplexity works this way by default; ChatGPT also uses RAG mechanisms in web search.
In short: Even if an LLM does not know a brand from training, it can appear in answers via RAG – provided the content is crawlable, current, and well structured.
Structured Data / Schema Markup
Structured data are machine-readable annotations in the HTML of a page that help AI systems and search engines classify content correctly. FAQ markup, HowTo schema, or Product schema are classic examples.
Why is it so important for AI? – AI systems prefer content that they can understand without interpretative effort. Structured data provide exactly that – a kind of instruction manual for crawlers and models.
Token
The smallest unit in which a language model processes text. Depending on the language, a token corresponds approximately to a word or a syllable.
Why this is relevant: AI answers have a token limit – anyone who wants to be mentioned in these limited answers must deliver content that is concise and citable.
Zero-Click Search
Zero-Click Search refers to search queries in which users no longer visit a website – because the answer appears directly in the SERP or in the AI Overview. With the expansion of AI Overviews, this share is increasing.
What exactly does that mean? Fewer clicks do not automatically mean less relevance. Anyone cited as a source in AI Overviews gains visibility and trust – even without direct traffic. The metric is shifting: away from the click, towards the mention.
Your Future is in AI’s Hands – and yours!
Brands no longer navigate only traditional search engines, but a multi-layered AI ecosystem. Visibility today is created:
- In AI Overviews on Google
- In generative answers on ChatGPT, Perplexity, and others
- In LLM knowledge bases
- Through citations and source references
Anyone who does not appear in AI answers today will lose reach, relevance, and customers tomorrow. AI is not a distant future – it is already the channel through which your target audience makes decisions, evaluates brands, and finds information. The question is not whether you have to engage with it – but when you start.
Conclusion? Not yet! – AI is evolving, and so is this glossary
New concepts such as GEO, LLMO, or AI Reading often emerge faster than teams can adapt their strategies. This glossary is therefore continuously updated – with new terms, fresh practical examples, and the latest developments from the AI ecosystem.
