When someone asks ChatGPT, Google AI Overviews, Perplexity, or another AI search engine a question, the answer may look simple.
Behind that answer, however, several things can happen before the response appears on the screen.
The system may interpret the question, expand it into several related searches, retrieve information from different sources, evaluate those sources, select supporting content, and then generate an answer.
For marketers, understanding this process is becoming increasingly important.
It explains why traditional search rankings don’t tell the whole story anymore and why what is GEO is a question every modern search marketer should understand.
Generative Engine Optimization (GEO) focuses on improving the likelihood that your brand, website, products, services, or content will be understood and referenced by AI-powered search experiences.
This guide breaks down the process in plain English.
What Is GEO?
GEO stands for Generative Engine Optimization.
Traditional SEO focuses heavily on helping pages appear in search engine results pages. GEO focuses on how brands and content appear within AI-generated answers.
When an AI search system responds to a user’s question, it doesn’t simply display a list of ten blue links. It may combine information from multiple sources and generate a conversational response.
That creates a different visibility challenge.
Your goal is no longer just:
“Can my page rank for this keyword?”
It is also:
“Can an AI system find, understand, trust, and cite my content when answering this question?”
This is the foundation of GEO.
How AI Search Retrieves Content
To understand GEO, it helps to understand what happens after someone enters a question into an AI search system.
The process can be simplified into four major stages:
USER QUESTION
↓
QUERY UNDERSTANDING
↓
QUERY FAN-OUT
↓
CONTENT RETRIEVAL
↓
SOURCE / CITATION SELECTION
↓
AI RESPONSE GENERATION
↓
USER SEES ANSWER
Not every AI system uses exactly the same process, and some systems combine or skip certain steps.
But this model provides a useful framework for marketers.
Step 1: The AI Understands the Question
Imagine someone searches:
“What is GEO and how can a B2B software company improve its visibility in ChatGPT?”
A traditional search engine might primarily match the query against pages containing relevant words and concepts.
An AI search system can go further.
It needs to understand the meaning and intent behind the question.
The user isn’t simply looking for a definition of GEO.
They may also want:
- An explanation of GEO
- Examples
- Practical strategies
- Information relevant to B2B companies
- Advice about ChatGPT visibility
- Potential next steps
This means marketers need to create content that answers the underlying question not just content that repeats the target keyword.
Step 2: Query Fan-Out Expands the Search
One of the most important concepts in AI search is query fan-out AI.
Instead of treating the user’s question as one search, an AI system can break the question into several related searches or information needs.
For example:
Original question:
"What is GEO and how can a B2B software company improve
its visibility in ChatGPT?"
↓
QUERY FAN-OUT
┌────────────┬────────────┐
↓ ↓ ↓
What is GEO? GEO strategy ChatGPT
visibility
↓ ↓ ↓
Definitions B2B GEO AI citations
↓ ↓ ↓
Examples Best practices Brand mentions
The system can then retrieve information relevant to these different parts of the question.
This is important for marketers because a page doesn’t necessarily need to match the exact wording of a user’s question.
It needs to cover the concepts and relationships that help answer the question.
Why Query Fan-Out Matters for Marketers
Consider two pages.
Page A repeats:
GEO, GEO, GEO, GEO.
Page B explains:
- What GEO means
- How AI search works
- How AI systems retrieve information
- How citations are selected
- How brands can improve their content
- Examples of GEO strategies
Page B provides much more context.
If an AI system breaks a complex question into multiple related information needs, comprehensive content can potentially provide useful material for several parts of the answer.
This is one reason topical depth matters in GEO.
Step 3: RAG Retrieves Relevant Information
The next important concept is RAG, or Retrieval-Augmented Generation.
The name sounds technical, but the idea is relatively simple.
RAG allows an AI system to retrieve relevant information before generating an answer.
Think of it like giving an employee access to a research folder before asking them to write a report.
Without the folder, they rely mainly on what they already know.
With the folder, they can look up relevant information and use it in the response.
A simplified RAG process looks like this:
USER QUESTION
↓
SEARCH / RETRIEVAL
↓
RELEVANT CONTENT
┌────┼────┬────┐
↓ ↓ ↓ ↓
Site Docs News Other sources
└────┼────┴────┘
↓
SELECT USEFUL INFORMATION
↓
GENERATE RESPONSE
This is where RAG for SEO becomes particularly interesting.
Your website isn’t necessarily being evaluated only as a traditional search result.
Its content may become part of the information retrieved to help construct an AI-generated answer.
What Is RAG for SEO?
RAG for SEO is best understood as the intersection between content retrieval and AI-generated responses.
The important question becomes:
Can an AI system retrieve the information on my website when it needs to answer a relevant question?
This creates several practical content considerations.
Your content should be:
- Clear
- Specific
- Well structured
- Factually useful
- Easy to interpret
- Relevant to identifiable questions
- Supported by credible information
- Consistent with your broader website content
This doesn’t mean there is a guaranteed “GEO ranking factor” you can optimise for.
AI systems vary, and their retrieval and ranking mechanisms aren’t fully transparent.
But marketers can make their content easier for machines and humans to understand.
Step 4: The System Evaluates Retrieved Sources
Retrieval doesn’t necessarily mean every piece of content found becomes part of the final answer.
The system may have many potential sources to consider.
It needs to determine which information is useful for the question.
This is where LLM content ranking becomes an important concept.
Traditional search ranking and AI retrieval aren’t necessarily the same thing.
An AI system may consider factors such as:
- Relevance to the question
- Quality of information
- Authority
- Freshness
- Context
- Consistency with other sources
- How directly the content answers the question
The exact signals differ between systems.
That’s why marketers should avoid thinking of GEO as simply “SEO with a different ranking position.”
It is a broader visibility problem.
Step 5: Citation Selection
Some AI search experiences provide links or citations showing where information came from.
This creates another important stage:
RETRIEVED SOURCES
↓
Which sources best support the answer?
↓
CITATION SELECTION
↓
AI-GENERATED RESPONSE
↓
CITATIONS / LINKS SHOWN TO USER
A source can therefore be valuable in two ways.
It can help the AI understand a topic, and it can potentially be surfaced as a citation or supporting source.
This is why AI citation strategy deserves its own place in a GEO programme.
Marketers should think beyond simply getting pages indexed.
They should also consider whether their content contains useful, supportable information that an AI system could confidently reference.
Why Citations Matter
Imagine a user asks:
“What is the difference between SEO and GEO?”
An AI system might produce a concise explanation and cite several sources.
For the brands included in those citations, the exposure can be valuable.
The user may:
- Read the cited page
- Visit the company
- Search for the brand
- Explore its services
- Remember the company later
This is one of the major opportunities created by AI search.
Visibility isn’t limited to occupying a position on a traditional search results page.
Step 6: The LLM Generates the Final Response
After relevant information has been retrieved and selected, the system can generate the final response.
This is the part users actually see.
The process can be simplified as:
QUESTION
↓
UNDERSTAND INTENT
↓
FAN OUT INTO RELATED SEARCHES
↓
RETRIEVE RELEVANT CONTENT
↓
EVALUATE INFORMATION
↓
SELECT SUPPORTING SOURCES
↓
GENERATE ANSWER
↓
CITATIONS / SOURCES
The final response isn’t necessarily copied from one webpage.
The system can synthesise information from multiple sources into a new response.
That distinction is extremely important.
AI Search Is Not Just “Ranking #1”
One of the biggest misconceptions about AI search is that there must be a single position equivalent to Google’s number one ranking.
AI-generated answers can work differently.
A response may draw from several sources.
One brand might be mentioned in the answer.
Another might receive a citation.
Another might provide information that influences the response without being prominently cited.
This means marketers should measure AI visibility more broadly.
Useful questions include:
- Is my brand mentioned?
- Is my website cited?
- Which pages are being cited?
- For which questions?
- Which competitors appear alongside me?
- What topics does AI associate with my brand?
- Are the answers accurate?
- How often does my brand appear across relevant prompts?
What Does This Mean for Content Strategy?
Understanding retrieval changes how marketers should approach content.
The objective isn’t to write pages exclusively for an algorithm.
Instead, create content that provides genuinely useful information in a structure that both people and machines can understand.
Answer Specific Questions
Strong GEO content should directly answer the questions your audience asks.
Instead of hiding the answer behind several paragraphs of introduction, make the answer clear.
For example:
What is GEO?
GEO stands for Generative Engine Optimization. It is the practice of improving a brand’s visibility and representation in AI-generated search experiences.
A direct answer like this is useful to readers and provides a clear definition of the topic.
Build Topic Depth
One page rarely needs to answer every possible question about a subject.
Instead, build related content around important topics.
For example:
GEO
│
┌──────────┼──────────┐
↓ ↓ ↓
AI Search Citations Technical
│ │ │
↓ ↓ ↓
Retrieval Citation Schema
& RAG Strategy Markup
│
↓
Query Fan-Out
This creates a connected topic cluster.
Individual pages can answer specific questions while collectively demonstrating depth around the broader subject.
How Schema Markup Can Help
Schema markup isn’t a magic button for getting cited by AI.
However, structured data can help search engines understand what a page represents and how different entities and pieces of information relate to one another.
For marketers working on GEO, schema should therefore be considered part of the broader technical foundation.
Depending on the content, structured data can describe things such as:
- Organisations
- People
- Articles
- Products
- Services
- FAQs
- Events
The goal is clearer machine understanding not simply adding as much schema as possible.
Content Clarity Matters
AI systems need to interpret your content.
That makes clarity especially important.
Compare:
“Our innovative solutions leverage transformative methodologies to create enhanced outcomes.”
with:
“Our platform helps B2B marketing teams measure their visibility in AI search.”
The second statement communicates a specific idea.
It is easier for a person to understand and easier to associate with a particular concept.
For GEO, specificity is valuable.
Build Content Around Entities and Concepts
Keywords still matter, but marketers should think beyond individual keyword phrases.
AI systems can connect concepts.
For example, a page about GEO might naturally discuss:
- Generative Engine Optimization
- AI search
- Large language models
- Retrieval
- Citations
- ChatGPT
- Google AI Overviews
- Perplexity
- RAG
- Structured data
- Brand visibility
These aren’t simply additional keywords.
They help establish the context of the topic.
How LLMs Retrieve Content: A Simple Marketing Model
If you’re not technical, remember this simplified version:
1. THE USER ASKS A QUESTION
↓
2. AI UNDERSTANDS THE INTENT
↓
3. AI EXPANDS THE QUESTION
INTO RELATED INFORMATION NEEDS
↓
4. AI RETRIEVES RELEVANT CONTENT
↓
5. AI EVALUATES THE INFORMATION
↓
6. AI SELECTS SUPPORTING SOURCES
↓
7. AI GENERATES THE ANSWER
↓
8. USER SEES THE RESPONSE
+ POSSIBLE CITATIONS
Your job as a marketer is not to control every step.
You can’t.
Instead, your job is to make your website a strong source of useful, understandable and credible information that can contribute to those steps.
What Marketers Should Optimise for in AI Search
A practical GEO strategy should focus on several areas.
1. Relevance
Does your content answer the questions your target audience is actually asking?
2. Depth
Does your website demonstrate meaningful knowledge about the subjects associated with your products or services?
3. Clarity
Can a reader quickly understand what you do, who you serve and what your content means?
4. Authority
Does your content demonstrate credible expertise and provide useful evidence?
5. Structure
Is your content organised logically with descriptive headings, concise answers and clear relationships between topics?
6. Technical Accessibility
Can search engines and AI systems access and understand your important content?
7. Consistency
Does your brand communicate the same facts and positioning across your website and other reputable sources?
These elements work together.
There is no single optimisation trick that guarantees AI citations.
Common GEO Mistakes
Writing Only for Keywords
Keyword targeting remains useful, but repeating a phrase doesn’t automatically make content valuable to AI systems.
Focus on the question and the underlying topic.
Creating Thin AI Content
Publishing large quantities of generic AI-generated articles isn’t the same as building useful topical authority.
Content should provide original value, clear explanations, evidence and meaningful expertise.
Ignoring Technical SEO
GEO doesn’t replace technical SEO.
If search engines can’t access, crawl or understand important content, retrieval can become more difficult.
Treating Citations as the Only Goal
A citation is valuable, but it isn’t the entire GEO strategy.
Brand mentions, accurate representation, visibility across relevant questions and referral traffic can also matter.
Forgetting the User
The ultimate audience is still a human.
If content is confusing, repetitive or unhelpful, optimising it for AI systems doesn’t solve the underlying problem.
The Bigger Picture: GEO Is About Being Useful to AI Search
The simplest way to understand GEO is this:
Make your brand and content useful, understandable and credible enough to be considered when AI systems answer relevant questions.
The technology behind AI search is complicated, but marketers don’t need to become machine-learning engineers.
You need to understand the basic journey:
QUESTION
↓
FAN-OUT
↓
RETRIEVAL
↓
EVALUATION
↓
CITATION
↓
GENERATION
Once you understand that journey, GEO becomes much easier to approach strategically.
Instead of asking only:
“How do I rank this page?”
you can start asking:
“What questions does my audience ask AI, what information does the AI need to answer them, and does my website provide that information clearly?”
That is a much more useful question for the future of search.
How to Improve Your Visibility in AI Search
Start by identifying the questions your customers are asking across ChatGPT, Google AI search experiences, Perplexity and other AI platforms.
Then examine whether your website provides strong answers to those questions.
Look for gaps in:
- Definitions
- Comparisons
- Product information
- Industry questions
- Supporting evidence
- Author expertise
- Brand information
- Structured data
- Related topics
- Citation-worthy content
From there, build a connected content strategy that strengthens your authority around the subjects that matter most to your business.
Your AI Citation Strategy should form part of that process, while Schema Markup can support the technical side of making your content easier for search engines to interpret.
Frequently Asked Questions
What is GEO?
GEO, or Generative Engine Optimization, is the practice of improving a brand’s visibility and representation in AI-generated search results and answers.
How do LLMs retrieve content?
LLM-powered search systems can retrieve relevant information from online sources and other available data before generating a response. The exact retrieval process varies between platforms.
What is query fan-out AI?
Query fan-out is a process where an AI search system expands a user’s original question into multiple related searches or information needs to gather a broader set of relevant information.
What is RAG for SEO?
RAG, or Retrieval-Augmented Generation, combines information retrieval with AI response generation. In search, this can allow an AI system to retrieve relevant content before producing an answer.
Does SEO still matter for GEO?
Yes. Technical SEO, crawlability, indexing, site structure and content quality can all remain important foundations for AI search visibility. GEO builds on these foundations rather than simply replacing SEO.
What is LLM content ranking?
LLM content ranking refers broadly to how AI-powered systems determine which retrieved information or sources are most useful for answering a particular query. The exact mechanisms and signals vary by platform and are generally not fully disclosed.
Can I guarantee that ChatGPT will cite my website?
No. There is no reliable method that guarantees a particular website will be cited by ChatGPT or another AI search system. GEO should focus on improving relevance, credibility, clarity and discoverability rather than promising guaranteed citations.