SEO vs GEO

SEO is about ranking webpages on a search engine results page (SERP) – picking keywords, optimizing content, and building backlinks. GEO, on the other hand, is optimizing your content to be directly included or cited in AI-generated answers.

In other words, instead of fighting for Rank #1, you’re aiming to become the source that a generative AI search engine (like Google’s SGE or ChatGPT) uses to answer users’ questions. The way people search is changing fundamentally: users are getting AI-generated answers at the top of search results, which often means fewer clicks on the "classic blue links". In fact, Gartner predicts that by 2028, brands’ organic search traffic will drop by 50% or more as consumers embrace generative AI-powered search.^† This is a wake-up call: to remain visible, marketers need to optimize not just for humans and algorithms reading their pages, but for AI systems synthesizing answers.

Let’s break down how we got here and what it means for your marketing strategy.

How Large Language Models Work

To understand GEO, you first need a basic grasp of large language models – the AI brains behind tools like ChatGPT, Google’s Bard, and the new generative search engines.

An LLM is essentially a machine learning model trained on huge swaths of text (think: the entire internet, books, articles, YouTube videos, you name it) to do one main thing – predict what text comes next. If you feed it a prompt like “SEO is evolving because…”, the model uses everything it “learned” during training to continue the sentence in a human-like way. Modern LLMs are built using a type of neural network called a transformer (not the Hollywood kind of Transformer, but a neural network architecture). The transformer’s secret sauce is a mechanism called “attention” that lets the model focus on the parts of the input that are most relevant to predicting the next word. By learning these patterns of language and meaning, LLMs can generate everything from a simple answer to a complex article.

Training and scale: Training an LLM is like teaching a child language by reading them all the books in the library. The model starts with random knowledge and gradually adjusts itself (through countless examples and massive computing power) until it becomes very good at guessing words and sentences that sound coherent and relevant. The result is a model that can carry on a conversation, write code, or answer questions based on the probabilistic patterns of language. The bigger the model (measured in parameters and training data), generally the more fluent and knowledgeable it becomes. That’s why you hear about model names with billions of parameters – scale tends to bring more “knowledge” and better performance.

Limits of LLMs: Importantly, an LLM doesn’t truly understand text like a human does – it doesn’t have beliefs or direct access to live information on its own. It’s generating educated guesses. This means they sometimes “hallucinate” (produce incorrect information that sounds confident) if they haven’t seen reliable info on a topic. To mitigate this in search, generative search engines combine LLMs with real-time retrieval of information from the web (more on that later) through a process called RAG.

Fun fact: Because interacting with LLMs is all about giving the right text prompts to get good answers, one AI expert quipped that “English is the hottest new programming language.” In other words, mastering how you ask questions (or how you structure content for AI) is becoming as valuable as say, knowing how to code.

Generative AI in Search Engines: How Does GEO Differ?

Classic search engines like Google crawl and index webpages, then use ranking algorithms (with hundreds of factors) to decide the order of results. In that world, SEO practices evolved to please those algorithms – from using relevant keywords in your title, to earning authoritative backlinks, to ensuring your site loads fast. The user performs a query, and the search engine returns a list of links (with snippets). The user then clicks through to whichever result seems best.

Generative search engines flip that script. When you ask a generative search engine, like Google's SGE, the engine doesn’t give you a list of links – it gives you a synthesized answer. It does this by using an LLM under the hood, and augments the answer with real-time information. Here is how that works:

1. Internet Search — First, the search engine finds relevant webpages via its index (SEO isn’t dead, you still need to be in the index and have relevant content).

2. LLM Processing — Instead of showing those pages directly, the engine then feeds snippets of those pages into an LLM which generates a summary or answer that addresses the user’s query. In Google’s case, it might use several of its advanced models (like PaLM 2, MUM, etc.) fine-tuned for search tasks, to ensure the answer is accurate and cites sources. The LLM is trained to output answers with references – you’ll notice AI answers often include citation numbers or links to the websites they pulled information from.

3. The final output — An “AI overview” or answer paragraph that draws from multiple sources, often with footnote links. The user can expand it, ask follow-up questions, or click through the cited sources if they need more detail.

In essence, the search engine is doing the reading and content synthesis for the user. This is convenient for users, but it’s a new challenge for content creators. Your content could be used to answer a question without the user ever visiting your site – and sometimes without even an explicit citation (if the LLM paraphrases it).

The GEO Mindset: Given this change, focusing solely on getting a #1 ranking is not enough. You need to ensure your content is AI-friendly – that it can be easily found, understood, and credited by the generative algorithms. This is the core of Generative Engine Optimization (GEO). It’s about optimizing for inclusion in answers rather than higher positions on a SERP. A marketer's question changes from “How do I rank for this keyword?” to “How do I become the trusted source that an AI will cite when answering this query?”

Mindset: SEO vs. GEO

Adopting GEO means adjusting some long-held assumptions from the SEO world. Here’s how the mindset shifts:

• From Keywords to Intent and Context: In SEO, you might obsess over finding the perfect keyword and using it enough times. In GEO, keywords still matter (the AI can’t cite you if you’re not relevant), but it’s more important to match the user’s intent and provide comprehensive context. Remember, the AI is looking for the piece of text that directly answers the user's question. Think in terms of the questions users ask and how to answer them directly. For example, instead of just targeting “best time to post on social media” as a keyword, ensure your content actually answers “What is the best time to post on social media and why?”.
• From Click-Through Rates to Citation Rates: In SEO, success is measured by how many people click your link from Google’s results. In GEO, success is when your brand/content is mentioned or cited in the AI-generated answer. If the AI answer says “According to YourSite… [information]”, that’s a win. It’s brand visibility without a click. So, you’ll start caring about metrics like citation frequency or answer presence – basically, how often and how prominently your content gets used by generative engines.
• From Backlinks to Facts & Structure: Old-school SEO treats backlinks as gold for authority. LLMs, however, don’t have an inherent concept of backlink count when generating answers. They care more about information quality and how easy it is to extract. That means having well-structured content (clear headings, bullet points, concise paragraphs), factual accuracy, and yes, even citing authoritative sources within your content. Including a relevant statistic from a reputable source (and referencing it) not only makes your content stronger for readers, but an AI model parsing your text will recognize it as well-sourced, factual information – exactly the kind of snippet it loves to present to users.

References:

1. Aggarwal et al. (2024). Generative Engine Optimization. Princeton University – Research report introducing GEO and its impact on content visibility. (Available at generative-engines.com/GEO)
2. Gartner (2024). AI in Marketing: How to Prepare for the Future of Search. Gartner Research Webinar/Report – Predicts impact of generative AI on search usage and organic traffic decline by 2028.
3. Aggarwal, P., Murahari, V., Rajpurohit, T., et al. (2024). “GEO: Generative Engine Optimization.” Proc. of ACM SIGKDD 2024. – Academic paper outlining the GEO framework and reporting metrics like subjective impression scores and citation rates for optimized content.
4. Varn, “How does Google’s Search Generative Experience (SGE) work?” Bristol Creative Industries (Feb 2024) – Overview of Google’s generative search and how it integrates LLMs (MUM, PaLM2, etc.) to produce answer summaries.
5. Jain, A. “SEO is Dying. GEO is How You Stay Seen.” Clixlogix Blog (June 2025) – Analysis of GEO for marketers, summarizing the GEO research paper and offering practical tips (e.g. the importance of citations, structure, and paraphrase-resistant content).