What is Generative Engine Optimization (GEO)?

The world of SEO and website optimization is changing. With the introduction of artificial intelligence (AI) and large language models (LLMs), we now have to find new ways to understand how to optimize our websites for our users and generative engines (GEs). This is where generative engine optimization (GEO) comes into play.

Here, we are breaking down everything we currently know about GEO and how GEs function to learn and output information to users. We’ll also cover some newer approaches to content that serve to optimize website content for GEs, helping you integrate GEO into your SEO practices.

What is GEO?

GEO is defined as generative engine optimization, a new approach that integrates SEO best practices with more nuanced website optimization strategies to help websites rank better in organic, AI, and LLM user searches.

What are AI and LLMs?

LLMs are a class of AI systems designed to understand, generate, and manipulate human language at scale. The most popular LLMs include:

• ChatGPT: OpenAI’s LLM, which excels at conversational dialogue, context-aware responses, creative content generation, and wide-ranging knowledge retrieval for diverse user queries.
• Gemini: Google’s LLM excels at multimodal understanding, integrating text, image, and code processing to power complex reasoning and creative, innovative workflows.
• Perplexity: Perplexity AI’s LLM specializes in real-time question answering, delivering concise citations, accurately generated web insights, and transparent source attribution.
• Claude: Anthropic’s LLM is well-suited for context-rich, long-form dialogue, safety-focused instruction, nuanced reasoning, and maintaining coherence over detailed, extended conversations.

AI search engines predominantly include Google’s new AI Search, now being tested in the United States, and AI Overviews, another Google product that focuses on finding the most authoritative and direct sources of information to answer user queries at the top of the search engine results page (SERP).

What is the difference between SEO and GEO?

SEO

SEO is a digital marketing strategy to improve a website’s visibility in search engine results like Google and Bing. The primary goal of SEO is to increase organic traffic to a website by optimizing three key pillars:

1. On-page SEO: Optimizing content, keywords, and meta tags to improve topical authority and relevance.
2. Technical SEO: Improving elements such as site speed, mobile-friendliness, and crawlability.
3. Off-page SEO: Generating backlinks and authority building.

GEO

Unlike SEO, GEO is the emerging practice of optimizing content to rank or appear prominently in AI-generated responses from tools like ChatGPT, Gemini, or Perplexity. The difference in optimization strategies for GEO compared to SEO includes:

• Providing clear, structured answers to likely user queries.
• Using natural, conversational language to match AI model patterns.
• Embedding citations or sources that large language models are trained on.
• Optimizing for retrieval-augmented generation (RAG) systems that pull from web sources.
• Focusing on authority and clarity to increase the chances of being used in model outputs.

Why should we care about GEO?

GEO is reshaping traditional SEO by adapting to the rapidly growing use of AI and LLMs for search. While both technologies aim to provide users with the most helpful information, they gather and deliver content differently: LLMs rely on static, pre-trained internet data, whereas AI models can continually update by crawling live sources. This shift in how information is sourced and interpreted is driving a new standard for content optimization. To stay visible and relevant, websites must now tailor their strategies to meet the needs of both AI-driven tools and LLMs.

4 reasons to care about GEO

• GEO challenges and complements traditional SEO.
• LLMs use static training data; AI models often update dynamically.
• Both aim to serve the most helpful content, but use different methods.
• Optimizing for AI and LLM visibility is now essential for websites.

How do AI and LLMs learn?

To understand how to approach GE optimization, we must first grasp how these engines learn and what they examine when crawling online sources of content. Each GE operates in a similar capacity, while serving a somewhat different purpose, which is what diversifies the output of each GE when asked a similar question. Let’s get into how some of the most prominent GEs learn and select the information they showcase to us, the users.

How Google’s AI Overviews learns

AI Overviews is Google’s latest generative engine, primarily focused on summarizing authoritative content for informational queries. It functions similarly to the traditional algorithm, prioritizing high-quality content and trusted sources, with content quality as the main ranking factor and domain authority as a key secondary signal.

AI Overviews focuses on summarizing the key points and ideas relating to the user’s query, often citing sources that directly answer the query with a near-definitive answer. It features:

• Summary overview: A summary of all the relevant information from websites in the SERP.
• Main ideas: A few bullet points highlighting the key ideas found across the most relevant sources.
• Expanded overview: An extended version of the summary with more detail and citations.
• Linked sources overview: A section of the AI Overview with a list of sources and the information gathered from them to help answer the query.

How LLMs learn

LLMs operate much like a math equation or an algorithm. They don’t really “think,” so we cannot really call them artificial intelligence. Instead, LLMs use a series of computations and condense all the information fed to them into a series of numbers, similar to binary code and vectors.

In LLMs like ChatGPT, vectors, also known as embeddings, are used to convert words and text into numerical formats that allow the AI to understand meaning and context. While their technical function is complex, their application in GEO is straightforward. Embeddings group similar keywords and phrases (e.g., “car” and “automobile”), giving writers greater flexibility to use natural language without relying on repetitive keyword stuffing to rank.

The impact of AI and LLMs on SEO

Using SEO tools like Semrush and Ahrefs, our team has been actively tracking the progress of client websites on SERPs. We have been monitoring SERP data for clients from January 2025 to July 2025, and we have found that there has been a substantial increase in AI Overview features and LLM referral traffic to websites across various industries. This started as a gradual increase as Google started integrating AI Overviews into its platform. Now we are seeing that AI Overviews can be found on as much as 30% of all searches, and over 70% of informational or problem-solving search queries, according to Search Engine Journal.

LLM referral traffic is rising as users increasingly depend on AI to streamline decision-making and information discovery. Key drivers include:

• Decision support: Users rely on LLMs to guide decisions and answer complex queries.
• Local discovery: LLMs help identify relevant service providers near users’ locations.
• Search integration: LLMs are merging with search engines, expanding their reliability and reach.
• Information generation: Their core function is to generate accurate, context-aware information.
• Efficiency focus: Designed to reduce time-to-answer and minimize the need for follow-up searches.

We have seen examples of more users relying on AI and LLMs for daily workout planning, meal ideas, research papers, studying, and even some extremes like using these platforms as a replacement for therapy, as reported in this article by Business Insider. While this trend is increasing at an alarming rate, it underscores how our strategies need to adapt in order to provide the most relevant and accurate information to these tools, for the benefit of users and our own.

SEO will forever be transformed by this new technology, and the evolution of SEO is what will unlock its full potential as GEO, or even organic and generative engine optimization (OGEO).

How can we adapt to GEO?

Adapting SEO practices to fit GEO is fairly straightforward, but it will change the fundamental way we structure content online. As mentioned previously, the goal of AI and LLMs is to reduce the time between search and solution or answer. That’s because, much like users, AI and LLMs are looking for the same thing: “The Solution.”

We’ve seen how an abundance of content that goes on and on without getting to the point can turn most users into bouncers, leaving a website without finding their answers or even understanding the content. You can picture these GEs as these users, in the sense that they can be impatient and quick to act. So, what does this tell us exactly?

Writing content for GEO requires a shift toward extreme clarity and efficiency, favouring a simple, no-fluff style that delivers fast, definitive answers aligned with how generative engines process and rank content. Key principles include:

• Direct answers: Match queries with direct responses (e.g., “Why” → “Because”, “Where” → “Here”).

• Speed to relevance: The faster your content delivers the answer, the more likely a GE will cite it.

• Verification matters: GEs cross-check your answer against others to validate its accuracy.

• Authority signals: Domain and page authority still play a role in helping GEs justify which content to reference.

• Simplicity wins: Clear, concise writing beats long-winded explanations in GEO contexts.

What’s next for GEO?

GEO is the evolution of SEO, driven by the rise of AI and LLM search behaviour. As users seek instant, reliable answers, content must prioritize clarity, authority, and directness to rank in both traditional search and AI-driven results like AI Overviews. Success now depends on meeting the needs of both humans and machines.

Based on the evolving relationship between GEO and SEO, we can make several predictions for the next few years:

1. GEO will become central to content strategy as AI-driven tools, such as AI Overviews, gain more influence over how users access information, thereby reducing reliance on traditional search results.
2. Content will prioritize clarity and precision over length or keyword density, with direct, authoritative answers becoming the standard for ranking in both generative engines and traditional SERPs.
3. Dual optimization will be necessary, as sites must craft content that appeals to both human readers and AI systems.
4. Authority will carry even more weight, with domain and content credibility playing a critical role in whether AI models surface or cite specific sources.
5. Search behaviour will shift toward conversational, intent-based queries, pushing SEO professionals to adapt their approach to mirror how users engage with LLMs and AI assistants.
6. Metrics for success will evolve, with visibility in AI-generated summaries (like AI Overviews) becoming as important as traditional organic rankings.

GEO is not replacing SEO. It is changing it in a big way. To stay ahead, your content must meet the rising demands of both AI systems and real users. This shift calls for more than just content optimization; it requires a complete transformation in content structure and delivery.

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