When ChatGPT came out back in late 2022, it didn’t immediately change the way people searched for information online.

It turned heads for sure, but it lacked live access to the internet, and most people still relied on Google’s organic search results to find online content.

The big change occurred in 2024 when Google introduced its AI Overviews powered by its in-house LLM, Gemini. Suddenly, the Google results page was dominated by AI-generated responses and citations, causing a sharp uptick in zero-click searches.

Evidence shows that at the end of 2024, 60% of all searches ended with no clicks.

By early 2025, Google made AI Overviews more prominent, sparking the AI search revolution that we’re smack dab in the middle of today.

At this point, LLM-powered search is no longer something business owners can ignore.

Since LLMs function quite differently from search algorithms, a new marketing methodology was also necessary.

Speaking of which, there’s been a bit of a naming bonanza going on in the search world. Read a few articles, and you’ll see names like AI SEO, AEO, LLM SEO, and AIO.

These all refer to the same thing: optimizing your content to appear in more AI-generated responses.

To get rid of the confusion, we’re planting a flag and sticking with generative engine optimization, or GEO for short.

What is Generative Search Optimization? How is it Different from Regular SEO?

Formally defined, GEO is the process of optimizing your brand’s content and online presence so that you’re accurately recognized, referenced, and cited by generative AI systems like ChatGPT, Perplexity, and Claude. 

It’s the AI equivalent of traditional SEO, and it involves appealing to large language models (LLMs) instead of search algorithms. 

Think about it like this: 

1. SEO is about improving your visibility in indexed search results 
2. GEO is about improving your visibility in generated responses from AI tools 

GEO is crucial for search marketing now because both search algorithms and LLMs are intrinsically linked now. 

First, AI Overviews (AIOs) appear for a majority of queries on Google, and they now appear for all types of search intent. 

They’ve caused zero-click searches to skyrocket and organic traffic to plummet. The appearance of an AIO can cause your CTR to drop by over 30%. 

Visibility in the AIO itself is the remedy, as is improving your visibility on other AI platforms like ChatGPT and Perplexity. 

Secondly, search algorithms and LLMs share a symbiotic relationship. 

Traditional search algos like Google and Bing refine ranking results by analyzing user engagement data from LLM-generated responses. That means LLMs are helping make search algorithms more efficient. 

At the same time, LLMs are calibrated with fresh content crawls, search query logs, and click behavior. That’s not to mention that they rely on existing search engine indexes to pull online content in the first place. 

Since search algorithms and LLMs now work hand-in-hand, ignoring GEO and focusing only on SEO won’t work anymore. 

Regardless of your brand, industry, or marketing approach, you need to incorporate elements of GEO in order to remain visible and relevant online. 

Here are some of the differences and similarities between traditional SEO and GEO (although the lines continue to blur each day):

What are the Benefits of GEO? Is it Worth the Investment?

GEO is still a very new practice, which, understandably, makes some CMOs nervous about investing in it. However, enough research and experiments have been done at this point to prove its ROI. 

Here are some notable statistics:

1. According to Semrush, one LLM visitor is worth 4.4x the average organic search visitor. 
2. Ahrefs also discovered that traffic from AI search tools converts at higher rates.  
3. Getting cited in an AI Overview has the potential to improve your CTR by 35% (Seer Interactive). Remember, NOT getting cited causes CTRs to dip by 34.5%, so AIO visibility is extremely important. 

Moreover, with strong entity optimization and airtight GEO, you can convince LLMs that your brand is the most authoritative and trusted in your niche. Because of this, AI platforms may begin to recommend your products and services directly. 

As you can imagine, this is an extremely powerful position to hold. People trust AI recommendations, and they’ve become incredibly popular tools for online shopping. 

In short, GEO leads to higher levels of brand awareness, customer trust, and AI traffic converts exceptionally well. Those three reasons alone are worth investing in it as a marketing practice. 

How Does GEO Work? What Makes LLMs Tick?

If you want to master GEO, you need to understand how LLMs decide who to trust and what to cite when generating responses. 

It’s also helpful to know what AI models don’t do, especially in terms of breaking old SEO habits. 

LLMs do not:

• Match static keywords 
• Count backlinks (or use the link graph in general) 
• Use composite authority scores like Domain Authority or Domain Rating 

As you can see, bread and butter SEO tactics like keyword usage and backlink volume don’t work in GEO. Also, as search algorithms continue to get more advanced, they rely less and less on the factors mentioned above. 

Here’s what LLMs actually do:

• Understand natural language, context, and the relationship between words and concepts 
• Identify entities and tie them to entries in knowledge databases 
• Combine existing knowledge from training data with RAG (retrieval-augmented generation) to integrate  
• Evaluate a brand’s online mentions, backlink quality, relevance, and user reviews 

Knowledge of these elements is key to learning how to optimize your brand’s online presence for AI search platforms. 

Here’s a closer look. 

Entity recognition, understanding, and linking 

When AI models generate answers to user prompts, they aren’t chasing specific keywords. Instead, they’re looking for the entities behind them. These are the people, places, and things that give meaning to a prompt and reveal the user’s intent.  

This process is called entity recognition, and it lets LLMs:

1. Eliminate ambiguity from words with double meanings (apple the fruit and Apple the company) 
2. Retrieve relevant facts about an entity, like the location of Nike’s headquarters (and other useful information) 
3. Understand the relationships between concepts, people, organizations, and brands (like how
4.  Ahrefs is related to SEO and digital marketing). 

Before natural language processing (NLP) and named entity recognition (NER), search algorithms operated with static strings of text. They matched exact keyword phrasing without fully knowing what the words meant. 

LLMs are able to fully understand language, context, and meaning, so things like exact match keyword phrasing are no longer necessary. 

Knowledge graph integration

Online education tree concept with e-learning training resources icons vector illustration

Knowledge graphs and databases are how LLMs verify entities and their connections to other entities. 

Google has a major Knowledge Graph, but it’s far from the only source that AI models use. There’s Wikidata, Wikipedia, and Crunchbase, to name a few. 

Not only that, but companies like OpenAI and Anthropic are steadily working on their internal knowledge graphs (which have the potential to eventually rival Google’s). 

Regardless, knowledge graphs make the entity recognition process possible, so they’re massively important in GEO. 

Namely, developing a strong entity footprint across the web makes your brand highly ‘linkable’ in these graphs (i.e., getting LLMs to associate you with your niche and related concepts). Combined with genuinely helpful, well-structured content, improving your entity profile is a powerful way to earn more AI citations. 

Using RAG to combine pretrained knowledge with external data sources (online content)

At first, ChatGPT wasn’t able to access the internet and relied on pre-existing knowledge from its training data. While it was trained on countless articles, academic papers, books, movie scripts, and all manner of publicly available data, its knowledge of current events had a hard cutoff. 

The challenge was to find a way to connect AI platforms like ChatGPT to integrate knowledge from live sources like the internet without the need for retraining. 

That’s where retrieval-augmented generation (RAG) entered the picture. 

Put simply, RAG is the bridge that enables LLMs to combine:

1. Static knowledge from training data and internal knowledge graphs 

With:

1. Dynamic information accessed from the web and other real-time sources 

To clarify, RAG is not the connection tool; it’s the integration tool. The actual fetching of online data occurs through APIs, plugins, and web scrapers. 

For example, if an AI model receives a prompt about financial SaaS tools, it could begin by recalling its existing knowledge about reliable financial software tools. Then, using RAG, it can incorporate fresh, real-time information gathered through APIs or web scraping. 

Lastly, the LLM synthesizes both sources of information to formulate the best, most up-to-date answer to the user’s prompt. 

Evaluating trust signals (brand mentions, reviews, brand sentiment) 

Trust is one area where GEO differs from regular SEO, while still sharing some similarities, which can be a bit confusing. 

In the old school PageRank way of doing things, the link graph acted as the penultimate authority system. Each link from one site to another carried a trust signal, giving backlinks the moniker of ‘credibility votes.’ 

This system was largely syntactical and numerical. If a domain had lots of backlinks, it was deemed credible, so long as the links didn’t come from manipulative sources. 

If a website scored a link from a major website like The New York Times, it would cause its perceived authority to rise, regardless of the context. 

In GEO, LLMs are savvy enough to understand the context behind your backlinks and brand mentions. If an authoritative website brings up your name but it’s completely irrelevant, it won’t count towards your brand’s authority. 

Also, AI models don’t consider link graph signals. Backlinks still matter as a trust signal, but contextual relevance reigns supreme. That’s why you should abandon composite scores like Domain Authority and Domain Rating. 

Also, authoritative brand mentions are the strongest trust signal for LLMs, and this is backed up by research. 

Your brand sentiment is also massively important, so you need to manage your reviews and online reputation (more on this in a bit). 

Implementing GEO Best Practices: Transitioning from Traditional SEO

By now, you know how GEO works and that it yields impressive results. The only thing left is to learn the best practices of GEO so that you can start forming new habits (and breaking old ones). 

Here’s a checklist for transitioning to GEO.

• Build a foundation for your brand’s entity  

You need to ensure that your brand is accurately represented in LLMs’ knowledge graphs (and the databases they use). 

First, identify your brand’s core entities, which are the essential people, products, and concepts related to your business. Of course, your brand name is a huge part of this, and you should ensure naming consistency. 

It’s very important that your brand goes by the same name everywhere that it appears online. 

For instance, if your website says Ted’s Designs LLC, you don’t want your social media pages to say Ted’s Designs Inc. (or any other variation for that matter). 

Why?

It’s because these discrepancies can cause LLMs to classify your business as two separate entities. 

Thus, consistency is key. 

Also, once you’ve identified your core entities, like your most important products, C-suite members, and concepts, make a detailed landing page for each of them. 

When doing so, link each page to authoritative external sources, like Wikipedia, LinkedIn, and Crunchbase. 

This whole step is about establishing a rock solid foundation for your brand’s entity profile, which is crucial for improving your citation frequency. 

• Implement sitewide structured data 

Imagine you have two options to choose from to learn the definition of a new term. 

The first page is a giant wall of text that has no breaks for paragraphs, subheadings, or bulleted lists. 

The next page has everything clearly labeled and formatted, and there’s even a glossary you can clock on. As a result, you can snap to the exact area to find the definition you need. 

We bring up this fictional scenario to demonstrate what it’s like for LLMs to encounter a page with no structured data (the first page) compared to one with structured data (the second page). 

Structured data formats like semantic HTML and schema markup clearly label aspects of your content to LLMs, like reviews, recipes, and author biographies. 

In actuality, they’re more like smart labels because they also let LLMs know the meaning behind your content (which helps remove ambiguity). 

• Publish genuinely helpful, context-rich content 

Content quality is now even more important than it was before. If you want your brand to get cited in AI responses, you need to produce relevant, engaging content that’s formatted to appeal to LLMs. 

Here are some tips for doing just that:

• Produce interlinked content clusters – LLMs respect topical authority, so you should publish clusters of content that cover the same topics in great detail. Ensure that you back these up with keyword research and prompt research so that you cover the topics that matter most to your target audience. 

• Use clear Q&As, subheadings, and bulleted lists – Concise formatting makes it easier for LLMs to parse and cite your content. Always answer the questions that you pose in the very next sentence, use proper subheadings (H1, H2, H3, etc.), and make frequent use of bulleted lists. Also, don’t forget to markup these elements with schema. 

• Publish original insights and first-hand experiences. Google’s E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) quality system still applies in the age of AI search. That means you need to stand out from the crowd with unique insights and real-world experiences. Interviewing industry experts and publishing original research are both extremely valuable, too. In the case of interviewing experts, LLMs will associate their expertise with your brand, so start rubbing elbows! 

Make sure your content contains structured data and internal links to get the most out of it. 

• Optimize for AI citations by improving trust signals  

The last step is to start building authority for your brand on AI search platforms. The best way to do this is to take a two-pronged approach:

1. Engage in digital PR-like tactics to continuously earn positive brand mentions and backlinks on relevant, trusted websites in your industry. 
2. Actively manage your brand’s online reviews and reputation across multiple platforms. 

This will simultaneously build a buzz for your brand while improving your reputation, both of which are necessary for earning the trust of AI models. 

Remember to monitor niche forums and platforms like Reddit to see what your audience has to say about your brand, too. LLMs will pay attention to community chatter, so you need to ensure that your sentiment is mostly positive. 

Final Thoughts: The Definitive GEO Guide

GEO is the new game in town, and it’s not going anywhere anytime soon. If you were hoping for a return to the traditional SEO days, your best bet is to transition to GEO now before it’s too late. 

Remember, search algorithms are learning from LLMs, so it’s only a matter of time until the search landscape has completely evolved into GEO on both fronts (search engines AND AI search tools). 

Are you ready to thrive with GEO?

Don’t wait to book a strategy call with our team to find out how.