Generative AI Engine Optimisation (GEO): What It Is and How It Works

Search is changing. As Large Language Models (LLMs) or generative AI tools like ChatGPT, Google’s AI Overviews, Microsoft Copilot, Siri and Gemini become mainstream, people are no longer just “searching” and clicking links. They are searching in different formats, asking questions and receiving direct, synthesised answers.

This shift has resulted in the creation of a new discipline: Generative AI Engine Optimisation (GEO).

GEO is about ensuring your brand, content and expertise are recognised, trusted and referenced by AI systems, not just ranked in traditional search results.

 

What is Generative AI Engine Optimisation?

Generative AI Engine Optimisation (GEO) is the practice of structuring and optimising online content, data and digital presence so that AI-powered search and answer engines can discover, understand, trust and reference your brand when generating responses. It focuses on enhancing brand authority, using natural language, and providing clear, authoritative answers to user queries. This is achieved by maximising visibility, citation, and inclusion.

Whereas SEO focuses on ranking webpages, GEO focuses on being:

• included in AI-generated answers
• accurately represented
• and, positioned as a trusted authority

Basically, SEO helps users find you online, whereas GEO helps AI speak for you.

 

Why GEO Matters Now!

Generative AI is already reshaping user behaviour:

• Searches are becoming longer and more conversational (long-tail searches)
• Fewer clicks happen when answers are delivered directly
• AI summaries increasingly sit above traditional search results on SERPs
• Brand visibility is shifting from “blue links” to mentions and citations

This means brands can lose visibility even while maintaining strong SEO rankings. If your content isn’t being used by AI engines to form answers, you risk not being a part of the conversation and disappearing from the decision-making process.

 

GEO VS SEO: What’s actual difference?

GEO is often framed as “the next SEO” – but that oversimplifies what’s really happening.

SEO	GEO
Optimises for rankings	Optimises for inclusion in AI answers
Focus on keywords and SERPs	Focus on entities, concepts and authority
Success measured in clicks	Success measured in mentions and citations
User reads your page	AI interprets and summarises your content

 

The technicalities differ, but the intent is the same: providing the best possible answer.

SEO and GEO Content: Different Outputs, the Same Goal

Despite the new terminology, SEO and GEO are not opposing disciplines. In practice, they are deeply aligned, and the strongest content strategies increasingly serve both SEO and GEO at the same time.

At their core, SEO and GEO share the same goal: to deliver clear, relevant and trustworthy answers to real questions.

The difference lies in who consumes that content.

Shared Foundations

Content that performs well in search engines already aligns closely with what generative AI systems value, which is why you might see some good AI visibility already without intentionally creating content specifically for GEO. Both reward content that is:

• Clear and well-structured
• Written for humans, and not algorithms
• Question-led and explanatory
• Expertise and authority heavy
• Accurate, consistent and current

If you’re producing high-quality SEO content today, you’re already laying the groundwork for GEO.

 

Where SEO and GEO Content Overlap

Shared Best Practice	Why It Works
Clear headings and structure	Helps users scan and helps AI analyse meaning
Question-based content	Matches how people search and how AI is prompted
Topical depth	Signals authority to search engines and AI models
Consistent terminology	Reduces ambiguity for humans and machines
Strong E-E-A-T signals	Improves rankings and AI trust

 

In many cases, a single well-written article (just like this one) can both rank well in search results and be summarised or cited by an AI engine.

 

The Key Difference Is the Outcome – Not the Content

The real distinction between SEO and GEO isn’t what you write, but how success is displayed. The success of SEO appears as your page ranks and earns traffic, whereas the success of GEO appears as your brand, insight or definition being included in the AI’s response.

As a result, GEO places slightly more emphasis on the following:

• Clear definitions
• Conceptual clarity
• Brand attribution
• Entity relationships

But the underlying content quality requirements remain remarkably similar.

 

How Generative AI Engines Use Content

To understand GEO in practice, it helps to understand how generative AI engines work at a high level.

1. Content Discovery

AI systems ingest information from:

• Authoritative websites
• Structured data (FAQs, how-tos, schemas)
• Reputable publishers and brands
• Publicly available and crawlable content

If content isn’t accessible or clearly structured, it may never enter the AI’s consideration.

2. Understanding & Context

AI looks beyond keywords to identify:

• Meaning and intent
• Relationships between concepts
• Consistency across sources

Any content that is thin, vague or overly promotional struggles here.

3. Authority & Trust Signals

Generative AI prioritises sources that demonstrate:

• Topical authority
• Expertise and experience
• Consistency across platforms
• Citations and recognition elsewhere

This is where brand strength and content quality intersect.

4. Answer Generation

When a user asks a question, the AI:

• Synthesises information from multiple trusted sources
• Generates a single, coherent answer
• May cite brands, organisations or experts directly
• Often doesn’t rely on traditional links

GEO is about earning inclusion in that synthesis.

 

How GEO Works in Practice

Signalling to LLMs

LLMs rely on vast amounts of publicly available data to learn how to understand and generate language, but they also increasingly respect explicit signals about how content should be accessed and used.

LLM.txt is an emerging, voluntary standard designed to sit alongside files like robots.txt, providing clear guidance to AI systems about a website’s content, purpose, ownership and usage preferences. While robots.txt informs crawlers what they can index, llm.txt is intended to help LLMs understand what the site represents, which content is authoritative, and how it should be interpreted or referenced. In the context of GEO, LLM.txt offers brands a proactive way to shape how AI models perceive and attribute their content, improving accuracy, trust and the likelihood of correct representation in AI-generated answers as these standards continue to evolve.

Optimising for Entities, Not Just Keywords

AI engines think in entities – brands, organisations, people and concepts. GEO focuses on clearly defining:

• Who you are
• What you do
• What topics you are authoritative in

This helps AI systems associate your brand with specific areas of expertise.

Creating AI-Readable, Human-First Content

GEO-friendly content is:

• Clear and explanatory
• Logically structured
• Written to answer questions directly
• Rich in context, not marketing fluff

If an AI can confidently summarise it, you’re on the right track.

Strengthening Brand Authority Signals

AI engines don’t just assess pages, they assess brands. This includes:

• Thought leadership content
• Expert authorship
• Consistent messaging across channels
• Mentions in credible publications
• Clear “about” and expertise pages

GEO is as much a brand discipline as a technical one.

GEO Trust

Trust is the cornerstone of a digital presence, and it is critical to attracting reliable clicks. In a GEO context, trust refers to the confidence that AI systems, and ultimately users, place in your content and brand.

The trust users place in AI-generated responses is directly influenced by the perceived credibility of the sources the AI draws from. When an AI cites or summarises content from authoritative, consistent, and accurate sources, users are more likely to accept the information as reliable, even without visiting the original website.

Conversely, if AI responses appear vague, inconsistent, or sourced from low-quality content, users may question not only the answer but also the platform itself. For brands, this means that establishing trustworthiness in the AI ecosystem is no longer optional, it shapes user perception, engagement, and decision-making. High-quality, verifiable, and clearly structured content increases the likelihood that AI will represent a brand accurately, enhancing both user trust in the AI and trust in the brand itself.

Over time, consistent visibility in AI-generated responses builds a dual-layer of authority: the AI learns to treat the brand as reliable, and users internalise that reliability when making choices based on AI answers.

Measuring GEO Success

GEO doesn’t rely on clicks alone. Indicators include:

• Brand mentions in AI-generated answers
• Accuracy of brand descriptions
• Frequency of citation
• Ownership of key topics in AI responses

Measurement is still evolving, but visibility and representation are the core signals.

The Future of GEO

Generative AI isn’t replacing search, it’s reshaping it.

In the future:

• Visibility will matter more than traffic
• Authority will matter more than rankings
• Brands will compete to be summarised, not just found

Companies investing in clarity, expertise and trust now will shape how AI understands their brand going forward.

Final Thoughts

Generative AI Engine Optimisation isn’t a replacement for SEO – it’s the natural next layer on top of it.

SEO helps people find answers.
GEO helps AI explain answers.

The brands that win will be the ones producing content that search engines rank and AI engines trust.