AI Basics: Acronyms, Meanings and Reality – GEO, AEO, AIO, LLMO and more.

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What are the key terms, the meanings and context? What is the reality? SEO continues to follow grounding principles in the age of AI, regardless of acronyms, evolving technology, and new tools.

Definitions of AI search-related terms

First, a rundown of the commonly used terms and acronyms.

LLM – Large Language Model

An LLM (Large Language Model) is a collection of language databases designed to connect words via training through trillions of text data points. It serves as the information base for many AI Chatbots and enables chatbots to analyse, understand and generate natural language. Well-known examples include ChatGPT, Gemini and DeepSeek.

These models form the basis for many new search functions, such as AI-generated answers in Google search (AI Mode). They are changing how content is searched for, presented and interpreted, and as a result, the work of SEO professionals.

LLMO – Large Language Model Optimisation

LLMO refers to the attempt to optimise content so that it can be better processed, cited or integrated into responses by LLMs. In principle, it is a specialisation within classic content optimisation: structured content, clear semantics, verifiable sources.

GEO – Generative Engine Optimisation

GEO is meant to describe optimisation for generative search engines – search engines that do not only link to answers but generate them themselves, for example through AI Overviews on Google or chat-based answers in ChatGPT.

Geo can also apply to geographical locations, such as in geo-IP or geocaching. It is also the name of a popular publication in Germany and part of a shortened version of a very popular media brand – National Geographic.

AIO – AI Optimisation

AIO generally refers to the optimisation of content for use by AI systems. This ranges from prompt engineering to preparing texts for generative models. Concrete standards or guidelines are mostly lacking.

However, AIO is already common in the software industry, usually meaning “all-in-one”. In the SEO world, it also appears in connection with automated analysis (“automated insights optimisation”) and AI Overviews. It remains to be seen whether this abbreviation will establish itself.

AEO – Answer Engine Optimisation

This term is not new either. AEO has so far referred to the optimisation of content for so-called answer systems, particularly for featured snippets or voice search. The goal is to structure content so that it can be answered directly within the search engine without an additional click.

AEO also stands for “Authorised Economic Operator”, according to Wikipedia, a status a company can apply for from customs authorities. The term is therefore already in use globally.

Why these terms distract from the real task

Many of the abbreviations mentioned are intended to suggest innovation, but they distract from the core issue. Why?

• They give the impression of an entirely new discipline. In reality, they are usually just variations of well-known optimisation strategies.
• They obscure what really matters. Good content, clean technical setup, and clear user guidance remain crucial.
• They imply specialised expert knowledge. The message behind all these terms often has a sales angle: calling oneself “GEO” suggests expertise in AI and the ability to optimise for it, while sticking with “SEO” implies using old-fashioned methods. In executive boards, this narrative can gain traction but, at least in during the early phase of AI search, it is misleading, because nobody really knows yet how to appear in AI-generated answers.

“As in the early years of Google optimisation, much is still unknown in AI optimisation. Firstly, because the field is very young. Secondly, because everything is evolving very quickly.” (Johannes Beus)

GEO is fundamentally changing SEO

It is indisputable that the customer journey is shifting away from traditional search engines towards generative systems such as Google AI Mode, ChatGPT, Perplexity, or Gemini. Users no longer research across numerous websites and platforms because they expect fast, direct, and accurate answers through a continuous dialogue. Those who are not mentioned there dramatically lose visibility and relevance. This requires a rethink for SEO as well. Some old tactics no longer work in the new AI-driven world.

The frequently recommended measures, structure, timeliness, E-E-A-T, and presence in trusted sources, are not a new discipline. They have been core elements of any solid SEO strategy for years and will become even more central in the future.

Becoming visible with SISTRIX

Visibility remains a measurable metric even in the new search landscape. What changes is the way it is determined. Instead of tracking rankings for individual keywords to drive more website traffic, other metrics are now coming into focus – ones that received little attention in traditional SEO.

Arguably the most important new metric is brand mentions. This shows how often, for which prompts, and in what context a brand is mentioned in generative search systems.

For example, if a company offers pet food that stands out for particularly sustainable production, it can track how often its brand appears in responses to prompts such as “What is the best sustainable dog food?” This makes it possible to see how visible a brand currently is, which topics are already working, and where optimisation potential exists.

The SEO fundamentals remain the same, but the analysis is changing.

With the SISTRIX AI Chatbot Beta, this new form of visibility can already be measured today. When creating an individual monitoring project you can either use your own set of prompts or SISTRIX can automatically generate suitable prompts based on the identified brands, entities, and competitors. From that point on you will have a view on how often a brand appears in generative search systems and how this presence develops over time.

What AI really changes in SEO and what it doesn’t

• New formats in the SERPs: Search engines like Google no longer show just traditional lists of results, but also AI-generated answers. In AI mode, search engines compile content from multiple sources, effectively becoming competitors to your own content.
• Changed click behaviour: When the answer already appears in the search results, the incentive to click through to a website decreases. Early studies indicate that there will be significantly fewer clicks for informational queries in the future. This changes the relevance of the click-through rate (CTR) and requires a more nuanced view of visibility and traffic.
• Visibility in generative systems: For content to appear in AI systems, it must not only be high quality, but also prepared technically and semantically so that it is accessible to LLMs. This means:
  • Clear structure
  • Explicit context
  • Verifiable sources

Why it is still SEO

SEO stands for Search Engine Optimisation, meaning the optimisation of content for all search systems. Whether these systems appear as a traditional web search or as a chatbot does not change the principle: content should be discoverable and helpful.

The requirements for content in the age of AI, relevance, structure, authority, are not new. Only the emphasis has shifted. Those who have been practising good SEO for years and have regularly adapted their methods to the new conditions through Google updates are likely already well-positioned for generative search systems.

GEO, LLMO and other terms do not describe new professions or disciplines; they are marketing terms intended to sell a service. Which term will ultimately prevail is still uncertain.

If you understand SEO, you don’t need a new qualification, but rather an evolution of the strategies and tools. It is also important to honestly and transparently highlight the limits of what can still be influenced in the future and what cannot.

“Relevance cannot be created, arranged, or manipulated directly; at best, the conditions for relevance formation can be influenced.” (Karl Kratz via LinkedIn)

FAQ – Frequently Asked Questions about AI, SEO, and Relevance

Is it sensible to focus SEO entirely on AI?
No. AI-powered search and answer systems are an important part of modern search behaviour, but they do not replace traditional SEO work. As long as search engines serve as a data source and users submit queries, SEO remains central. A complete shift to “AI optimisation” is currently neither strategically nor technically advisable.

Can I influence whether my content appears in AI-generated answers?
Not directly. You can only influence the conditions under which a language model might pick up your content, for example through visibility in search engines, clean structure, clear language, and verifiable sources. Which content an LLM ultimately uses is decided by the model itself based on internal processes.

What role does structured data play in the AI context?
Structured data help both traditional search engines and language models better classify content. They increase the likelihood that content is correctly interpreted and potentially used in AI-generated answers. However, they do not provide a guarantee of visibility.

Are language models reliable when it comes to facts?
Not always. LLMs operate on probabilities and generate plausible but not always correct statements. Without source verification, an answer is not automatically trustworthy. It is therefore especially important to check AI-generated answers, particularly in a business context.

Can rankings in AI systems be deliberately manipulated?
No. Unlike traditional search engines, LLMs do not provide standardised output, and there are no rankings. The response to a prompt depends on the model, its wording, the context, and other internal variables. Attempts to manipulate the system are not only ineffective in the short term, but can also damage trust in your content in the long term.

How does relevance differ between search engines and LLMs?
Search engines calculate relevance based on documented signals (e.g., links, CTR, content). In LLMs, relevance is determined differently: the model itself decides what it considers “appropriate” for the input. This evaluation is internal to the system and does not follow external criteria that can be verified by third parties.