What is Generative Engine Optimisation (GEO)?

Generative Engine Optimisation, or GEO, is the practice of improving the likelihood that a brand or its content is understood, surfaced, mentioned or cited in AI-generated answers. Where traditional SEO focuses on ranking in a list of search results, GEO focuses on being part of the answer itself.

AI search platforms such as ChatGPT, Google AI Overviews and AI Mode, Gemini and Perplexity increasingly sit between a search query and the information someone finds. Instead of scrolling through ten blue links, people are getting a synthesised answer, often generated from multiple sources at once.

That shift matters because it changes how brands are discovered. Being ranked highly in traditional search results doesn’t automatically mean being mentioned or cited when someone asks an AI system the same question. GEO, sometimes also called generative AI optimisation or GEO optimisation, is how businesses adapt to that shift.

If you’ve been researching this topic, you’ve probably run into a few different terms that all seem to describe the same thing. GEO, AEO and LLM SEO aren’t three separate disciplines, they’re competing names for broadly the same shift, and no single term has become the standard yet.

Generative Engine Optimisation (GEO) refers to visibility within AI-generated answers, the term used throughout this article. Answer Engine Optimisation (AEO) is the older term, originating with featured snippets and voice search, and is now often used interchangeably with GEO. LLM SEO, or LLM optimisation, frames the same idea around the underlying AI models rather than the search experience itself.

Where the emphasis does genuinely differ: AEO grew out of direct-answer formats like featured snippets, while GEO grew out of generative systems that synthesise information across multiple sources. The terminology is still unsettled and may consolidate over time, we won’t predict which term wins.

At a high level, GEO follows a simple journey: discover, understand, retrieve, select, generate, cite or mention.

When someone asks an AI system a question, it may retrieve information from multiple sources across the web, assess which of that information is relevant, and generate a response that draws on it, sometimes citing or naming the sources it used. For content to make it into that response, it needs to be accessible to the AI system, clearly relevant to the question, easy to understand, and trustworthy.

It’s worth noting that AI responses can vary. The same question can produce a different answer depending on the platform, the exact wording of the query, or even when you ask it.

AI search visibility affects how your brand is discovered. When someone researches a product, service or topic using an AI assistant, your business either shows up as part of the answer or it doesn’t, regardless of how well you rank in traditional search.

This matters for a few reasons: brand discovery during research and comparison, recognition as a relevant entity in your space, potential referral traffic when an AI system links to your site, and influence over how information about your business is represented to the person asking.

That said, AI visibility isn’t a replacement for organic search, it’s an additional consideration that sits alongside it. Most people still use traditional search regularly, so the two should be thought about together rather than one instead of the other.

SEO and GEO overlap significantly, but they focus on slightly different things:

Primary environment: SEO operates in search engines; GEO operates in AI-powered search and answer experiences.

Core objective: SEO aims to improve organic visibility; GEO aims to improve visibility and representation in AI answers.

Focus: SEO prioritises relevance, technical SEO, authority and user experience; GEO adds clarity, entity understanding and easily extractable information on top of those same foundations.

Typical output: SEO produces a search result or ranking; GEO produces an AI-generated answer, mention or citation.

Relationship: strong SEO provides the foundation, and GEO builds on top of it.

The two approaches overlap far more than they diverge. Google’s current guidance confirms that foundational SEO remains relevant for generative AI features, so this isn’t a case of choosing one over the other.

There’s no single checklist that guarantees AI visibility, but a few consistent principles help.

1. Create clear, direct answers: Answer the question immediately rather than building up to it. Put the most important information first, use concise definitions and explanations, and avoid unnecessary filler before getting to the point.
2. Cover the topic and related questions: Address the main intent behind the question along with genuinely useful related questions. Demonstrate topical depth rather than repeating keywords, and think about how one question naturally leads to related follow-ups.
3. Make content easy to understand and extract: Use descriptive headings, keep paragraphs concise, and use lists or examples where they genuinely help. Present facts and claims clearly rather than burying them in dense paragraphs.
4. Demonstrate expertise and trust: Use credible sources, include evidence and original insights, and show clear authorship. Keep information accurate and up to date; this connects to the broader E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) principles search engines already look for.
5. Maintain strong technical SEO foundations: Make sure important content can be crawled and indexed, keep site architecture and internal linking clean, and use structured data where appropriate. Technical SEO isn’t a separate consideration from AI search, it’s part of the same foundation.

It can help to see how this plays out with real examples.

Example: an ecommerce brand

Traditional SEO might target a product or category query directly. GEO-aware content goes a step further, clearly explaining how products differ from one another, what they’re used for, their benefits and what someone should consider before buying.

Structured, factual information like this helps both human shoppers and AI systems understand exactly what’s on offer, rather than leaving them to infer it from marketing language.

Example: a B2B service business

Here, clarity starts with a plain definition of the service itself. From there, it helps to explain who the service is for, what problem it solves, what the process involves, the factors that affect cost, and how it compares to alternatives.

Backing this up with evidence, expertise, case studies or credible references gives an AI system reasons to trust and surface the content when someone asks a related question.

Measuring GEO is still evolving, so set expectations accordingly rather than promising precise numbers.

Useful things to track include brand mentions in AI-generated answers, whether your content is cited or listed as a source, which prompts tend to surface your brand, how you compare to competitors, how accurately your brand is represented, and referral traffic where it’s measurable across different AI platforms.

One important caveat: AI responses can vary between queries and even between runs of the same query. A single prompt or one-off check shouldn’t be treated as a definitive measurement; repeated testing across a broader set of prompts gives a much more reliable picture.

1. Treating GEO as a replacement for SEO: Strong SEO foundations still matter just as much as they did before; GEO builds on top of them rather than replacing them.
2. Writing content only for AI: Content still needs to work for the people reading it. Avoid unnatural keyword or question insertion that reads awkwardly to try to please an algorithm.
3. Chasing AI “hacks”: Be wary of tactics presented as guaranteed ranking or citation factors; AI search is continually evolving, and today’s shortcut can stop working tomorrow.
4. Measuring GEO from one prompt: AI responses are probabilistic and can change between runs. Use repeated testing and broader prompt sets rather than concluding from a single check.

GEO is best thought of as an extension of modern search strategy rather than a separate discipline competing for budget and attention. The SEO fundamentals you’ve likely already invested in, technical health, quality content, authority-building and internal linking, remain valuable and form the foundation GEO builds on.

What’s new is layering in AI visibility monitoring and prompt-based research alongside that existing activity. The overall focus doesn’t really change: create content that’s genuinely useful to people and search and AI systems will follow.

GEO helps brands improve visibility within AI-driven search experiences.

GEO does not replace traditional SEO, it builds on strong SEO foundations.

Clear, useful and authoritative content remains central to visibility, whether that’s traditional search or AI search.

Technical accessibility and strong SEO foundations still matter just as much as before.

AI visibility should be measured across a range of relevant prompts and platforms, not a single check.

The focus should be on becoming a genuinely trusted source, not on gaming AI systems.

Search is expanding beyond the traditional results page. GEO offers a way of thinking about how brands are discovered and represented when the “answer” is generated rather than clicked through to.

The principle to hold onto is simple: optimise for useful, trustworthy information first, and AI visibility tends to follow from strong foundations rather than clever workarounds.