GEO jargon explained in plain English

Generative Engine Optimisation (GEO) has picked up a lot of new vocabulary quite quickly. Some of it comes from academic research, some from AI platforms themselves and some from marketing vendors coining their own terms for the same idea. The result is a lot of overlapping language that can put people off engaging with the topic at all.

This glossary sets out the terms that actually matter, in plain English, without assuming any prior knowledge. Bookmark it and come back to it as you read the rest of our GEO content.

The core concepts

LLM

A large language model is the type of AI system behind tools like ChatGPT, Claude, Gemini and Perplexity. It’s trained on huge amounts of text to predict and generate natural language, which is what allows it to answer questions conversationally rather than just returning a list of links. Every other term in this glossary describes something an LLM does or something that shapes what it says.

GEO

Generative engine optimisation is the practice of getting your content cited inside AI-generated answers, rather than ranked as a link on a results page. Where SEO gets you a place in a list, GEO gets you a mention inside the answer itself.

GEO vs SEO

The two disciplines are related but distinct. SEO targets ranked positions using signals like backlinks and keyword relevance. GEO targets citation inside AI-generated answers using signals like clear factual claims, entity clarity and content structure. Good SEO still helps your GEO performance, but it isn’t the whole job on its own.

Zero-click search

This term explains a search where users get their answers directly on the results page or inside AI-generated responses without ever clicking through to a website. GEO and zero-click search are closely linked, since a citation inside an AI answer is often the only visibility a brand gets, with no guaranteed click to follow. 

AEO

Answer engine optimisation is the practice of writing content so that a short, self-contained passage can be lifted out and used as a direct AI answer. It’s a close relative of GEO, and the two terms are often used interchangeably, but AEO is more narrowly about single extractable answers while GEO covers the fuller synthesised answers a chat-based tool generates.

AI Overviews

Google’s AI-generated answer summaries appear above traditional organic results for eligible searches. They create content from multiple sources and cite each one, usually with a visible link. AI Overviews remain the largest single AI answer surface by volume of users who see them, though ChatGPT Search and Perplexity work in a similar way as standalone destinations rather than a feature bolted onto a results page.

How AI engines actually work

RAG

Retrieval-augmented generation sits behind most AI search tools. Rather than answering purely from what a model learned during training, a RAG system searches the web for relevant content in real time, then uses their findings to generate its answer and cite sources. Perplexity, ChatGPT Search and Google’s AI Overviews all run on some version of RAG.

Embedding

An embedding is a mathematical representation of a piece of text, turned into a set of numbers that capture its meaning. AI search tools convert both your search query and indexed web content into embeddings, then compare how close they sit to each other. Content that’s semantically close to a query gets retrieved, even if it doesn’t use the exact same wording.

Chunking

Long documents get broken down into smaller sections before they’re indexed, rather than being stored and retrieved as one whole page. This is chunking or segmentation. It’s a big part of why answer-first formatting and short paragraphs work so well in GEO, a model isn’t retrieving your whole article, it’s retrieving the specific chunk that best matches the query.

Metrics worth knowing

AI citation

This is when an AI-generated answer explicitly names or links to your brand as a source. It’s the core unit of value in GEO, similar to a backlink in traditional SEO, though the two aren’t directly comparable.

AI share of voice

This GEO metric is a measure of how often your brand appears in AI-generated answers relative to your competitors, across a defined set of relevant queries. This gives you a competitive benchmark rather than just a raw citation count.

Citation velocity

Citation velocity is how fast a brand is gaining new AI citations, rather than how many it has right now. A citation count tells you where you stand today. Velocity tells you whether that position is improving or slipping.

Worth a quick caveat here though. This isn’t an official, agreed-upon metric. It’s a term a few vendors have started using and each one measures it slightly differently. There’s no shared standard behind it yet. Treat it as a useful way to think about trend, not a fixed number you can compare against other brands.

Trust and structure signals

Entity

A clearly defined person, place, organisation, product or concept that an AI system can recognise and connect to other information about it. The clearer and more consistent your brand is as an entity across the web, on your own site, on Wikipedia, in press coverage and in structured data, the easier it is for a model to confidently cite you rather than a source it’s less sure about.

Knowledge graph

A structured database of entities and the relationships between them, which AI systems use to understand context. If your brand’s relationships to your products, people and industry are clearly and consistently represented across the web, it becomes easier for a model to place you correctly within its own understanding.

Hallucination

This is when an AI system generates false or fabricated information and presents it as fact. It’s a known limitation of generative models. Content that makes verifiable, well-sourced claims reduces the chance of a model misrepresenting your brand, since there’s less ambiguity for it to fill in incorrectly.

A living GEO vocabulary

This list isn’t final. GEO terminology is still settling, much like SEO vocabulary took a few years to stabilise in the 2000s. Some of these terms will likely be replaced or consolidated as the field matures and new ones will appear as AI search platforms evolve.

For now, understanding these fundamentals gives you a solid base for reading anything else written about GEO, including the rest of our content on this topic.

Want help putting this into practice?

If the concepts make sense but you’re not sure where to start applying them, our GEO strategy audit is a good next step. Get in touch and we’ll walk you through it.