GEO Without the Jargon: 8 Terms Every Brand Marketer Should Know

Part1: What GEO actually means

You don’t need to be an AI engineer to understand GEO. But if you work in brand, marketing or communications, you are going to hear a growing list of unfamiliar terms: crawling, schema, entities, embeddings, RAG, retrieval. At first, GEO can sound like a technical problem that belongs entirely to developers. But, it isn't.

At its core, Generative Engine Optimisation (GEO) is about making your brand’s information easier for AI-powered systems to find, understand, retrieve and use when answering a question.

If someone asks ChatGPT, Gemini, Perplexity or another AI-powered search tool: “What are the best accounting firms for startups in Sydney?” or “Where can I find a women’s health clinic near Zetland?” Your brand cannot simply “rank for a keyword” in the traditional sense. Before your information can become part of an AI-generated answer, several things need to happen. Machines need to access the content, understand what it is about, identify the relevant information and match that information to the question being asked. That is where most GEO terminology comes from.

Part2: 8 GEO terms every brand marketer should know

1. Crawling

In one sentence: Crawling is the process of automated systems visiting webpages and accessing the information available on them.

Before a search or AI system can do anything useful with your website, it first needs to be able to reach it.

Traditional search engines use automated programs called crawlers to discover and download webpages. Google describes crawling as the stage where automated crawlers discover pages and download their text, images and other resources before those pages can be processed further [GoogleSearchCentral].

Different AI companies also operate different crawlers for purposes such as search, retrieval and model training.

Example

Imagine your company has published an excellent guide answering a common customer question.

The content might be accurate, well written and highly relevant.

But if the systems that could retrieve it cannot access the page, none of that matters.

Files such as robots.txt, server settings and other access controls can influence which automated systems are allowed to crawl different parts of a website.

2. Semantic HTML

In one sentence: Semantic HTML gives a webpage a meaningful structure that helps machines distinguish different types of content.

People can look at a webpage and immediately recognise what is happening. We know that large text at the top is probably a headline. We can recognise a navigation menu, a service description, an author name or a list of FAQs largely from visual design. Machines do not experience websites in the same way. Semantic HTML provides structural signals that describe the role of different parts of a page [MDN].

Example

Imagine two clinic websites look almost identical. One clearly marks its page title, headings, sections and navigation. The other is built from generic containers with very little structural information. Both may look perfect to a human visitor. But the first provides clearer clues about how the information is organised. That matters because GEO is not only about how your page looks. It is also about how easily machines can interpret what each part of the page is doing.

3. Schema / Structured Data

In one sentence: Structured data gives machines explicit information about what something on a webpage represents.

This is where websites start becoming more explicit. Structured data uses standardised formats to describe information on a page. Google describes structured data as a way of providing explicit clues about the meaning and classification of webpage content [GoogleSearchCentral]. For marketers, the easiest way to understand schema is to think of it as labelling. 

Example

Without structured information, a machine has to infer what different pieces of content represent from the page itself. Schema can provide additional clues. It does not magically guarantee that an AI system will recommend your brand. But it can make important information less ambiguous and more machine-readable.

A useful way to remember the difference is:

HTML helps organise the page.
Schema helps describe what the information represents.

4. Entity

In one sentence: An entity is a clearly identifiable thing, such as a person, company, place, product, organisation or service.

Keywords are words or strings of text, while entities represent identifiable things such as people, places, organisations or products. This distinction is useful in GEO because modern search and AI systems increasingly rely on understanding entities and their relationships, not just matching exact words [LinkGathering]. 

Suppose your website repeatedly mentions:

“Dr Jane Smith”,
“Sun Health”,
“Zetland”,
and “Women’s Health”.

The useful information is not simply that these words appear frequently.

It is the relationship between them:

Dr Jane Smith works at Sun Health.

Sun Health located in Zetland.

Sun Health provides Women’s Health services.

Example

Imagine someone asks: “Which women’s health clinics are in Zetland?” Understanding the word “Zetland” is not enough. A system needs to connect a location with an organisation and potentially with the services that organisation provides. This is why GEO increasingly encourages marketers to think beyond individual keywords.

So always ask: Does the web make it clear who you are, what you do and how those things are connected?

5. Chunking

In one sentence: Chunking means breaking larger pieces of content into smaller sections that can be processed or retrieved independently.

One of the easiest GEO ideas to understand is to stop thinking about webpages as complete documents. A human might open a 2,000-word article and read it from beginning to end. An AI retrieval system may only need one small section from that article to answer a particular question. That section is effectively a useful “chunk” [Rankscale].

Example

Imagine your clinic has a long page explaining:

• General Practice

• Women’s Health

• Health Checks

• Vaccinations

• Mental Health

• Opening Hours

Someone asks:

“Does this clinic offer women’s health appointments?”

The system does not necessarily need the entire page. The most useful piece may simply be the section explaining the women’s health service. That is why clear headings, focused sections and self-contained explanations matter. The easier your content is to divide into meaningful pieces, the easier it can be to retrieve the right piece for the right question.

6. Embeddings & Semantic Search

In one sentence: Embeddings convert information into numerical representations, allowing systems to retrieve content based on semantic similarity rather than relying solely on exact keyword matches.

Traditional keyword matching asks: Do these words match?

Semantic search asks something closer to: Do these pieces of information mean similar things?

Embeddings are numerical representations of content that allow systems to compare semantic similarity between different pieces of text. Semantic retrieval can therefore surface relevant information even when the retrieved content does not use exactly the same wording as the original query [OpenAIEmbeddingsRetrieval].

Example

A user asks: “Where can I see a GP near Zetland?”

Your website says: “General Practice services available in Zetland.”

Those two sentences do not use exactly the same language. But semantically, they are very closely related. A system using semantic retrieval can potentially recognise that relationship. For marketers, this creates an important shift in thinking: Good GEO is not about repeating every possible phrase someone might type. It is about making the meaning of your content clear.

7. Retrieval & RAG

In one sentence: Retrieval finds relevant information for a question, while Retrieval Augmented Generation (RAG) uses retrieved information to help generate an answer.

This is where many of the previous concepts start coming together. When a user asks an AI system a question, some systems can search external sources or a connected knowledge base for relevant information. That process is called retrieval. The retrieved information can then be supplied to a language model to help it produce an answer. This broader pattern is commonly called RAG.

A simplified version looks like this:

User Question → Relevant Information Retrieved → Sources Provided to the Model → AI Generates an Answer

Semantic retrieval systems can use embeddings to identify information that is conceptually relevant even when the query and source do not share the same exact keywords [OpenAIRetrieval].

Example

Someone asks: “What should I consider when choosing a women’s health clinic in Sydney?” An AI-powered search system may retrieve information from several relevant webpages before generating its response. For a brand, this is one of the most important ideas in GEO: Being understood does not automatically mean being retrieved. Your content needs to be relevant to the specific question being asked. GEO therefore is not about somehow “putting your website inside ChatGPT”. It is about increasing the usefulness and retrievability of your information when the right question appears.

8. Citation

In one sentence: A citation is a visible reference to a source used to support an AI generated answer.

For brands, this is often the most visible outcome of GEO. You ask a question. The AI produces an answer. Next to part of that answer, you see a source. That source might be an article, company website, government page, news publication, research paper or another webpage. If your content appears there, it has become part of the information environment supporting the answer.

Example

A user asks: “What is GEO and how does it work?” An AI-powered search system generates an explanation and cites several sources underneath it. If your article becomes one of those sources, you have achieved something quite different from simply ranking on a traditional search results page. 

Part3: How these terms work together

GEO begins by making sure relevant systems can crawl your website and access the information you want to make discoverable. 

Clear semantic HTML gives that information structure. 

Schema and structured data can provide additional machine readable clues about what the information represents. Those clues help clarify important entities: your company, people, locations, products and services, and the relationships between them. 

Well-organised content can then be divided into meaningful chunks, allowing a system to work with specific pieces of information rather than an entire webpage. Through embeddings and semantic search, those pieces can be compared by meaning, helping a system connect your content with questions that may use completely different wording [OpenAI]. 

During retrieval, relevant pieces of information can be selected in response to a user’s question. In systems that use RAG, that retrieved information can then help a language model generate a more grounded answer. And when the system exposes the supporting source, your webpage may appear as a citation.

That is why GEO is bigger than adding a few keywords. It is about reducing friction between your information and the machines that may eventually use it.