GEO (Generative Engine Optimization) - What It Is, How It Differs from SEO and Where to Start

GEO (Generative Engine Optimization) means optimizing so that your brand is cited and recommended in answers generated by AI - in Google AI Overviews and AI Mode, ChatGPT, Perplexity or Copilot. SEO fights for a high ranking and a click, GEO for a place inside the answer itself. According to a 2025 Pew Research study, when Google shows an AI summary, users click a regular link 8% of the time instead of 15%, and a link inside the summary just 1% of the time. GEO doesn't replace SEO - it stands on SEO's foundation, because AI engines pick their sources from the same indexes you optimize for.

GEO means optimizing for citations in AI answers: Google AI Overviews, ChatGPT, Perplexity and Copilot. Where it came from, how AI picks sources, how it differs from SEO, research-backed tactics, measurement and a 10-minute GEO readiness test.

In this guide I explain where the term GEO came from, how AI engines choose sources, how GEO differs from SEO, what still works from classic SEO and what's losing ground, which tactics are backed by research and how to measure results. At the end you'll find a GEO readiness test you can do in 10 minutes.

Where GEO came from

The term Generative Engine Optimization was coined by researchers from Princeton, Georgia Tech and the Allen Institute for AI in the paper "GEO: Generative Engine Optimization" (Aggarwal et al., arXiv 2023), presented at KDD 2024. They built a benchmark called GEO-bench with 10,000 queries and measured which content changes increase visibility in AI answers. It's still one of the few sources of hard data in this field.

You'll also see AEO (Answer Engine Optimization), used almost interchangeably. The terminology isn't standardized. In this guide, GEO means all optimization for citation in generative engines.

Where GEO applies

GEO isn't about one product but a whole family of answer engines:

• Google AI Overviews and AI Mode - summaries above the results and a separate conversational mode, based on Google's index and the Gemini model, with links to sources.
• ChatGPT with search - combines training knowledge with live retrieval and cites several sources.
• Perplexity - searches live, ranks the results and writes an answer with footnotes on sentences.
• Microsoft Copilot - uses the Bing index.
• Claude, Gemini and other assistants - increasingly with web search.

What they have in common: each engine first retrieves a set of candidate sources from an index, then the model writes an answer based on them and adds citations.

How AI engines choose sources

The mechanism is called RAG (retrieval-augmented generation) with grounding. The engine searches for candidates, passes fragments of them to the model, and the model writes an answer based on those fragments, citing the ones it used most.

That leads to a hard dependency: to be cited, you have to be in the index the engine retrieves from. A page that can't be indexed in Google or Bing won't make it into the answer. Your chances improve with relevance to the query and its sub-queries, freshness, structure and how easily a fragment can be extracted, the authority of the author and site, consistent brand data, and confirmation on other trusted sites.

The GEO-bench authors tested what actually works. The most effective changes were adding citations to sources, adding expert quotes and adding statistics. They raised content visibility by up to about 40%. Keyword stuffing didn't help and sometimes hurt. One caveat: these are results from a controlled benchmark, not measurements on live Google or ChatGPT, so treat them as a direction, not a guarantee of exact percentages.

How GEO differs from SEO

The key point: the same foundations work in both cases, but AI gives more weight to clear entities and citable evidence than to the number of links alone.

What still works from SEO and what's losing ground

SEO isn't dead. What ended is the model where a Google ranking equals visibility. The foundations now work twice, because they feed both Google rankings and source selection by AI:

• Technical health. Speed (Core Web Vitals), indexability, correct headings, canonicals and a sitemap - without these, neither Googlebot nor AI crawlers will process the page.
• Structured data. Article, FAQPage, Organization or Product help search engines understand unambiguously who you are and what a page is about. Details in the Schema.org guide.
• Content that matches intent. A page that answers the question precisely gets picked by both Google and AI models.
• Links from trusted sources. They still affect rankings, and for AI they also act as outside confirmation.
• E-E-A-T. The author's experience and credibility count in both worlds. I cover how to build it in the post on E-E-A-T.

Losing ground:

• Keyword stuffing - helps neither in Google nor in AI.
• Long texts written for word count - retrieval systems split text into fragments and pick the ones with the highest information density.
• The featured snippet as a goal in itself - the AI summary often replaces it.
• Mass production of thin content - Google treats it as spam. More in the post on AI content and Google's policies.

What AI does to traffic

AI summaries reduce clicks on regular results. The scale depends on the study method:

• Pew Research (2025): 8% of users clicked a regular link when an AI summary was present, versus 15% without one.
• Ahrefs (December 2025): an AI summary lowers the CTR of the first-ranked result by 58% on average.
• Seer Interactive (September 2025): on informational queries with an AI summary, organic CTR fell from 1.76% to 0.61%. At the same time, brands cited in the summary got 35% more organic clicks and 91% more paid clicks than brands that weren't cited.

Google disputes some of these studies and stresses that it still sends billions of clicks. The direction is consistent, though: ranking alone delivers less and less, and being cited delivers more and more. I cover how to turn this into a business strategy in the post on the zero-click era.

GEO tactics with a solid basis

• Write fragments that can be cited. Start a paragraph with a direct answer and make each paragraph make sense on its own. Details in the guide on how to write content AI cites.
• Add data, quotes and sources. These are the three changes confirmed in GEO-bench.
• Structure your content. Descriptive headings, question-and-answer sections, lists and tables.
• Keep a consistent brand entity. The same company details everywhere, an author with visible experience, and structured data linking to your profiles.
• Build outside confirmation. Mentions in industry media, rankings, on Reddit and forums. More in the posts on digital PR and Reddit.
• Update your content. Freshness can be one of the selection factors.
• Let AI crawlers in. Check robots.txt for GPTBot, OAI-SearchBot, PerplexityBot and Google-Extended, and indexing in Google and Bing. Details in the guide on AI crawlers.

An example of minimal structured data that links a site to the brand's profiles:

{  "@context": "https://schema.org",  "@type": "Organization",  "name": "Your Company",  "url": "https://yourcompany.com",  "sameAs": [    "https://www.linkedin.com/company/yourcompany",    "https://www.wikidata.org/wiki/Q000000"  ],  "knowsAbout": ["process automation", "ERP integrations"]}

The sameAs property helps connect the site with profiles of the same company elsewhere. I cover llms.txt files in a separate post: no major engine has confirmed using them as a signal, so don't treat them as a visibility factor.

A three-level hybrid strategy

It would be a mistake to drop SEO for GEO - Google traffic is still the main channel for most sites. The right order is foundation first, then the layer on top.

Level 1 - the SEO foundation. A technically clean site, content that matches intent, internal linking that builds topical authority, and structured data on important pages.

Level 2 - AI-ready content. Key articles rewritten into direct answers and self-contained paragraphs, FAQ sections, complete structured data, and updating or removing low-value pages.

Level 3 - active GEO. Regularly checking what ChatGPT, Gemini and Perplexity say about the brand, building presence in outside sources, refining the brand entity (sameAs, Wikidata) and adjusting based on measurement.

Case study - B2B e-commerce in beauty and fashion, 6 months of hybrid strategy: a WooCommerce to PrestaShop migration combined with SEO and GEO brought organic traffic 23% above pre-migration levels, the domain's first citations in Perplexity for 4 key industry queries, and a rise in its share of AI answers across a set of 20 test questions from 0% to 18% in 5 months. The strategy paid for itself in 4 months.

How to measure GEO

You measure classic SEO by rankings and traffic. For GEO you need a few extra metrics:

• The AI report in Google Search Console. Since June 2026, Google has offered a report on impressions in AI Overviews, AI Mode and AI features in Discover, and at the end of August 2026 it rolled the report out to all sites. It shows impressions by page, country and date, but it doesn't show clicks.
• Share of AI answers (AI Share of Voice). The share of answers to your question set in which the brand appears, is cited or is recommended, compared with competitors. I explain how to do it step by step in the post on the AI visibility audit.
• Traffic from AI assistants in GA4. Visits from chatgpt.com, perplexity.ai or copilot.microsoft.com - instructions in the post on measuring AI traffic.
• Citations in Copilot. Bing Webmaster Tools shows where your site is cited in Copilot answers.

If you'd rather use a ready-made tool than manual tests, I compare them in the post on AI visibility monitoring tools.

A 10-minute GEO readiness test

Go through ten questions and count the "yes" answers. The test doesn't replace an audit, but it quickly shows where to start.

8-10 points: you have the foundation, so focus on outside mentions and regular measurement. 5-7 points: start with content and structured data. 0-4 points: technical SEO and indexing come first - without them GEO has nothing to stand on.

The most common mistakes

• Dropping SEO before building an alternative. Companies that stop investing in SEO often lose both channels at once.
• Believing good writing is enough. A great article without structure, data and outside confirmation rarely makes it into answers.
• No measurement. Optimizing without checking what models actually say is like running a campaign without looking at results.
• A one-off effort. Models change and competitors optimize too, so GEO is a process, not a project.
• Chasing tricks. Hidden instructions for AI or files promising a "ChatGPT ranking" have no confirmation and can hurt your credibility.

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What does it cost? An SEO and GEO visibility audit (a technical review of the site, citation tests in three AI models and a priorities report) costs PLN 1,500-2,500 net and takes 3-5 business days. Strategy and structured data implementation is priced individually.

I combine technical SEO with AI optimization (GEO), including an LLM citation strategy. I also teach it in the SEO & GEO course. Get in touch - I'll start with an audit of where you stand today in Google and in AI answers.

Worth reading next:

• Google AI Overviews & AI Mode - How to Optimize Your Site So AI Cites You
• AI Visibility Audit - Does ChatGPT Recommend Your Brand?
• How to Write Content AI Cites - Retrieval Structure: Chunks, Direct Answers, Tables
• E-E-A-T in 2026 - How to Build the Trust Google Rewards and AI Models Cite