GEO Explained: What Generative Engine Optimization Is, How It Differs from SEO, and How to Start
When people talk about GEO today, they usually mean Generative Engine Optimization: the practice of improving how a brand, product, or piece of content appears in AI-generated answers.
The term is often presented as “the next SEO.” That is too simplistic. GEO is related to SEO, but it addresses a different outcome.
SEO helps a page become discoverable and rank in traditional search results. GEO helps a brand or source become understood, selected, cited, mentioned, or recommended inside an AI-generated answer.
This article explains the concept, the difference between GEO and related disciplines, and a practical way to begin.
What Is GEO?
Generative search systems do not always return a list of ten blue links. Platforms such as ChatGPT Search, Perplexity, Google AI Overviews, and Google AI Mode can retrieve information from multiple sources and synthesize it into a conversational answer.
The process looks roughly like this:
User question
→ Query understanding and expansion
→ Web retrieval
→ Source selection
→ AI-generated synthesis
→ Citations, mentions, recommendations, and links
GEO focuses on the later stages of this process: whether the system uses your information when constructing its response and how it represents your brand.
The term was formalized in the research paper “GEO: Generative Engine Optimization,” first submitted in 2023 and later accepted to KDD 2024. The paper describes GEO as a framework for improving content visibility in generative engine responses and introduces GEO-bench, a benchmark for evaluating visibility across different domains. Its experiments found that optimization could improve visibility by up to 40% in the tested environment, while also showing that results varied by domain. This should be treated as a controlled research finding, not as a universal business-growth guarantee. [1]
How Generative Search Is Different
Traditional search generally presents a ranked set of pages. The user must compare results and decide which source to trust.
Generative search can perform several actions before presenting an answer:
- Interpret a conversational question;
- Break a complex request into related sub-queries;
- Retrieve information from different sources;
- Combine information into a summary;
- Recommend products, brands, or approaches;
- Link to selected sources.
Google describes this process as “query fan-out”: AI Overviews and AI Mode may issue multiple related searches across subtopics and data sources before producing a response. Google also notes that AI Overviews and AI Mode may use different models and techniques, so their answers and links can vary. [2]
OpenAI similarly describes ChatGPT Search as a conversational search experience that provides timely answers with links to relevant web sources. [3]
This changes the visibility question from:
“Where does my page rank?”
to:
“When an AI system answers a question relevant to my market, does it understand and include my brand?”
GEO vs. SEO
| Dimension | SEO | GEO |
|---|---|---|
| Primary goal | Improve visibility and ranking in traditional search | Improve inclusion, citation, recommendation, and representation in AI answers |
| Typical input | Keywords and search queries | Conversational questions, comparisons, and tasks |
| Typical output | Ranked search results | Synthesized answers with selected sources |
| Main unit of optimization | Webpage and keyword | Brand, entity, claim, source, and topic |
| Key metrics | Rankings, impressions, clicks, CTR, organic traffic | Mentions, citations, citation share, recommendation rate, narrative accuracy, AI referrals |
| Stability | Relatively structured and repeatable | More variable across platforms, prompts, locations, models, and time |
| Core technical foundation | Crawling, indexing, relevance, links, content quality | The same SEO foundation, plus answer clarity, source authority, entity consistency, and citation monitoring |
The relationship is therefore not “SEO versus GEO.” A better model is:
SEO provides the foundation for retrieval. GEO focuses on how retrieved information is used in the generated answer.
Google’s own documentation is important here. Google states that there are no additional technical requirements or special optimizations required to appear in AI Overviews or AI Mode. Traditional SEO best practices remain relevant, including crawlability, indexability, internal linking, textual content, page experience, and accurate structured data. Google also says that special AI files or special schema markup are not required. [2]
GEO should therefore not be understood as a replacement for SEO or as a separate technical trick.
GEO Is More Than “Writing for AI”
A common misconception is that GEO simply means adding more question-style headings, writing shorter paragraphs, or using a special content format.
Those techniques may improve clarity, but GEO is broader. A generative system may consider information from:
- Your own website;
- News and industry publications;
- Product directories;
- Reviews;
- LinkedIn;
- Reddit;
- YouTube;
- Comparison pages;
- Other third-party sources.
The result is that GEO involves not only website content, but also the broader information environment around a brand.
A brand may have a well-optimized website but still be poorly represented if:
- External sources describe it inconsistently;
- Competitors dominate comparison content;
- The company has little independent coverage;
- The product is associated with the wrong category;
- AI systems repeatedly receive outdated or incorrect information.
This is why current AI visibility products increasingly monitor not only owned pages, but also competitor sources, earned media, social content, and cited domains. Profound, for example, categorizes cited sources as owned, competitor, earned media, PR, social, and institutional sources. [8]
The Main GEO Outcomes
GEO is not a single outcome. It is useful to separate several layers.
1. Discoverability
Can the AI search system access and retrieve your content?
OpenAI recommends allowing OAI-SearchBot if a publisher wants its content to be discoverable and cited in ChatGPT Search. Perplexity provides similar guidance for PerplexityBot. [4][5]
2. Citation
Does the AI answer link to your website or a specific page?
A citation is stronger than a simple brand mention because it indicates that the system used your content as a source and may give the user a path to visit it.
3. Mention
Is your brand named in the answer, even without a link?
A mention may increase awareness, but it does not necessarily indicate that your website was used as the source.
4. Recommendation
Is your product presented as a suitable choice for a particular user, use case, market, or category?
Recommendation is usually more commercially meaningful than a neutral mention.
5. Narrative accuracy
Does the AI describe your product, positioning, audience, and limitations correctly?
A brand that is frequently mentioned but incorrectly described does not have healthy GEO performance.
How to Start with GEO
1. Build a prompt set based on real decisions
Do not begin with a generic list of keywords. Begin with questions your customers might ask before becoming aware of, comparing, or purchasing a solution.
A useful prompt set should include:
- Category questions: “What are the best tools for…?”
- Problem questions: “How can a small team solve…?”
- Comparison questions: “What is the difference between A and B?”
- Alternative questions: “What are the alternatives to…?”
- Audience questions: “Which product is best for a solo founder?”
- Brand questions: “What does this company do?”
- Transactional questions: “Which solution should I choose?”
This approach is consistent with how AI search platforms process conversational and multi-part queries, and with the prompt-tracking workflows offered by current AI visibility platforms. [2][6][7]
2. Maintain strong SEO fundamentals
Before optimizing for AI visibility, make sure search systems can access and understand your website.
Google recommends:
- Allowing crawling;
- Making important content available as text;
- Using clear internal links;
- Maintaining good page experience;
- Keeping structured data consistent with visible content;
- Publishing helpful, reliable, people-first content. [2]
You do not need a special “GEO page” or a special AI markup system. The priority is accessible, clear, trustworthy content.
3. Make important claims easy to understand and verify
AI systems need to identify what a page is saying and whether it is relevant to a question.
A practical content structure should:
- State the main answer clearly;
- Define important terms;
- Use descriptive headings;
- Separate facts, opinions, examples, and recommendations;
- Include specific evidence where appropriate;
- Keep product, audience, pricing, and category information consistent;
- Update information that changes over time.
The goal is not to make the article “sound like AI.” The goal is to make the information useful, unambiguous, and easy to retrieve.
4. Build authority beyond your own website
AI systems may cite third-party pages instead of a company’s own website. Therefore, GEO often requires building a broader source ecosystem:
- Independent industry coverage;
- Credible reviews;
- Comparison pages;
- Original research;
- Expert commentary;
- Customer evidence;
- Relevant community discussions;
- Consistent product information across important platforms.
This should be approached as genuine authority-building, not as mass-produced mentions or artificial review generation.
5. Monitor the answer, not just the ranking
A GEO audit should record:
- Whether the brand is mentioned;
- Whether the brand is cited;
- Which URL is cited;
- Which competitors appear;
- Whether the brand is recommended;
- How the brand is described;
- Whether the answer contains factual errors;
- Whether the answer changes over time.
A single manual search is not enough. Recent research on GEO measurement argues that AI visibility should be treated as a distribution rather than a single fixed ranking because answers can vary across prompts, runs, and time. [9]
How to Measure GEO Performance
A useful GEO scorecard can include:
Brand Mention Rate
Answers mentioning the brand
÷
Valid answers tested
Citation Rate
Answers citing the brand’s domain or pages
÷
Valid answers tested
Citation Share
The proportion of citations earned by your brand compared with competitors within the same prompt set.
Recommendation Rate
The percentage of relevant answers in which the brand is actively recommended rather than merely listed.
Narrative Accuracy
A qualitative or scored assessment of whether the AI describes the company correctly.
AI Referral Traffic
Traffic from AI platforms to your website.
Google reports AI feature traffic within the overall Web search data in Search Console. OpenAI also states that ChatGPT Search referral links include utm_source=chatgpt.com, allowing publishers to identify this traffic in analytics platforms. [2][4]
These metrics should be combined. A high mention rate with low accuracy may represent a brand risk. A high citation rate with no qualified traffic may indicate that the content is being used but not driving meaningful visits.
GEO Monitoring Tools
There is currently no single cross-platform, first-party “GEO Search Console” that measures every AI engine.
The market is made up mainly of third-party monitoring platforms:
- Semrush AI Visibility Toolkit: AI visibility, brand mentions, citations, prompt research, competitor analysis, brand performance, and technical AI-search audits. [6]
- Ahrefs Brand Radar: AI Share of Voice, cited pages and domains, custom prompts, and modeled visibility across multiple AI platforms. Ahrefs explicitly describes its visibility metrics as potential visibility rather than actual audience reach. [7]
- Profound: citation-level monitoring, source categorization, citation share, competitor analysis, and earned-media intelligence. [8]
- OtterlyAI: multi-platform monitoring, brand mentions, citations, competitor benchmarking, market and language coverage, and content audits. [10]
- Peec AI: AI visibility, share of voice, sentiment, citations, and competitor analysis across several AI search surfaces. [11]
These tools are useful, but their scores should not be compared directly without checking:
- Which platforms they monitor;
- Which countries and languages they cover;
- Whether prompts are real, modeled, or manually defined;
- How often they refresh data;
- Whether they query public interfaces or APIs;
- How they identify brands and citations;
- Whether their visibility metrics are modeled estimates.
Final Takeaway
GEO is best understood as the optimization of a brand’s presence inside AI-generated answers.
SEO asks:
Can the search engine find and rank my page?
GEO asks:
When an AI system answers a relevant question, will it understand, mention, cite, recommend, and accurately represent my brand?
The two disciplines overlap because AI search still depends on crawlability, indexability, relevance, content quality, and authority. But GEO adds a new layer: answer inclusion, citation behavior, brand narrative, and recommendation visibility.
The most reliable starting point is not to chase a single GEO score. Instead:
- Identify the questions that influence customer decisions.
- Test those questions across the AI platforms your audience uses.
- Measure mentions, citations, recommendations, and accuracy.
- Improve both owned content and third-party authority.
- Repeat the measurement over time.
GEO is still an evolving field, but its central principle is already clear:
Visibility is no longer only about ranking a page. It is also about becoming a trusted source in the answer.
References
-
Aggarwal, P. et al. “GEO: Generative Engine Optimization.” arXiv, accepted to KDD 2024.
https://arxiv.org/abs/2311.09735 -
Google Search Central. “AI Features and Your Website.”
https://developers.google.com/search/docs/appearance/ai-features -
OpenAI. “Introducing ChatGPT Search.”
https://openai.com/index/introducing-chatgpt-search -
OpenAI Help Center. “Publishers and Developers FAQ.”
https://help.openai.com/en/articles/12627856-publishers-and-developers-faq -
Perplexity Documentation. “Perplexity Crawlers.”
https://docs.perplexity.ai/docs/resources/perplexity-crawlers -
Semrush. “AI Visibility Toolkit.”
https://www.semrush.com/kb/1493-ai-visibility-toolkit -
Ahrefs. “Brand Radar: How We Collect and Model AI Visibility Data.”
https://ahrefs.com/blog/brand-radar-methodology -
Profound. “AI Citation Intelligence.”
https://www.tryprofound.com/features/answer-engine-insights/citations -
Schulte, J. et al. “Don’t Measure Once: Measuring Visibility in AI Search.” arXiv, 2026.
https://arxiv.org/abs/2604.07585 -
OtterlyAI. “AI Search Monitoring Tool Features.”
https://otterly.ai/features -
Peec AI. “AI Visibility and Share of Voice Tracking.”
https://peec.ai/product/ai-visibility
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