Journalist at Promodo
For over 6 years, I have been working as a journalist in the communications and marketing industry. I help brands communicate effectively through written content, engage with market experts, and create professional materials on topics related to business and marketing, sharing insights on working with marketing tools.
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GEO (Generative Engine Optimization) is the practice of optimizing content for generative search systems and AI assistants such as ChatGPT, Gemini, and Claude.
The rise of GEO is driven by a shift in user behavior: more and more people now turn to ChatGPT or Gemini to get answers to their questions. The goal of GEO is to make your business the AI's "favorite," so that it recommends you first.
In our new article, we’ll explain how GEO works, how it differs from classic SEO, and why businesses today need to optimize for LLMs.
Generative Engine Optimization (GEO) is the practice of optimizing content for generative AI tools, such as Google Gemini or ChatGPT, which craft their own answers to queries based on large volumes of analyzed information.
Unlike traditional SEO, which focuses on ranking pages in search results, GEO focuses on how content is perceived, summarized, and turned into direct answers within AI interfaces. In most cases, these direct answers appear specifically for informational queries.
This is exactly what business strategy needs to focus on now: not just writing text for search bots, but creating content that AI models can recognize, summarize, and use in their answers.
Even if a user doesn't click through to your site, if Google's AI cites your article or review as a source for its answer, you still win. This positions you as an expert in the consumer's eyes and can tip them toward buying from you specifically.
Important: GEO has nothing to do with geographic optimization (local SEO).
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Unlike traditional search engines, where the user gets a list of links and picks a source themselves, AI search immediately generates a ready-made answer based on information from multiple sites.
This happens in several stages: analyzing the user's query, retrieving information from the search index, verifying it, and only then generating the answer. Let's look at each stage in more detail.
The AI doesn't just search for and match keywords — it identifies intent, breaks a complex question into several sub-queries, and figures out what information needs to be found.
For example, the query “How do I optimize my online store for ChatGPT?” might be broken down into:
Content is a core part of GEO work. Site descriptions, product descriptions, specs, recommendations, and information about shipping and payment — all of it needs to be optimized for the user's search intent. In practice, you need to think in terms of your users' questions and needs, since relying on keywords alone no longer works.
Next, the model searches for relevant documents. To do this, it uses a search index. For example, ChatGPT Search pulls information from Bing's index, Claude draws on Brave Search, while Gemini and AI Overviews pull from Google's index.
What sources does it actually use? Information from brand websites and open sources such as forums, media outlets, and social media.
According to research by AirOps, 85% of brand mentions in ChatGPT, Claude, and Perplexity come from external sources, and only 13% come from the brand's own website.
Once the model finds relevant documents, it doesn't just copy information from the first source it comes across. The next stage is grounding — checking the answer against real sources.
At this stage, the model:
In other words, this stage checks how well your brand has worked on E-E-A-T
In the final answer, the LLM highlights key facts and adapts them to the user's query. If the platform supports links (ChatGPT Search, AI Overviews, Perplexity), it adds citations or a list of sources the user can click through to the business's site.
But even having your business name show up in an AI answer is already a good outcome of GEO work, since it also builds recognition and recall.
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Generative models don't browse pages the way people do. They look for clearly structured, concise wording that's easy to fold into a generated answer. That's why every paragraph needs to be informative, concise, and self-contained enough to be used out of context as a standalone snippet of an answer.
Formats that work well:
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How to demonstrate your expertise:
We also recommend creating a Wikipedia page for your company. It's a trust signal for both AI and SEO. But this may require help from specialists, since a self-created Wikipedia page can be removed by moderators.
LLMs don’t work with keywords but with context. While classic SEO focuses on exact query matches, AI search cares about related topics and the logical connections between them.
Context is everything surrounding a piece of content. Context can change everything fundamentally.
For example, “lying is bad” but what if you lye to a child their drawing is beautiful? That kind of “lie” actually encourages them to keep going and grow.
Generative search tries to form an answer that accounts for different points of view, which is why it picks the resources that appear most comprehensive and authoritative within their niche.
Generative Engine Optimization doesn't replace classic SEO. It rather builds on top of it.
Promodo's internal research found that LLMs pull information only from the top 10 results of the relevant search engine. They only search deeper if the top 10 don't provide enough information, which happens very rarely.
So if your key pages rank at least in position 10, that's already enough to show up in AI results. But if your site averages position 20 or lower, that points to serious SEO problems that will also block GEO results.
Important: your site needs to be indexed in Bing. To monitor optimization for this search engine and how results appear in ChatGPT, you need to connect to Bing Webmaster Tools.
Your site needs to be accessible to AI bots and load quickly.
Review your materials from the angles that matter for AI search:
GEO effectiveness is assessed using two main metrics: AI Visibility — the share of queries for which the AI's answer mentions your brand — and Share of Voice (SOV) — the share of brand mentions compared to competitors in your niche.
You can start measuring with two tools:
Find real GEO benchmarks by niche and see how many AI mentions sites in your category get.
To land in AI recommendations, you need to adapt to how it works. GEO has already become just as necessary as optimizing for Google search.
The main value of LLMs is that they help buyers decide what to buy and where. That's a funnel step you can't see in GA4.
Oleksandr Utkin
Promodo Service Development Manager
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GEO optimizes content for generative models, which craft answers to queries, while traditional SEO focuses on ranking pages in search results. However, to compete in GEO, your brand already needs to be in the top 10 of the relevant search engine, so SEO is a must.
Informational sites have already lost a significant share of their traffic, but for eCommerce there's no such threat. It's simply another channel for attracting traffic.
The first step is to run a GEO audit to check your site's current state, confirm whether it meets the basic requirements, and identify which queries LLMs recommend competitors for rather than your site. From there, focus on your content, adapting it to how AI ranking works and to what users actually need.
GEO (Generative Engine Optimization) helps a brand show up in the answers generated by models such as ChatGPT, Gemini, or AI Overviews.
AEO (Answer Engine Optimization) focuses more narrowly on making sure content gives clear answers to specific user questions. In practice, the two approaches complement each other: AEO is part of an effective GEO strategy.
Yes, but not as directly as in classic SEO. Quality mentions and links from authoritative sites strengthen trust in the brand and help AI assess its expertise. For GEO, it's not just active links that matter — plain text mentions of your brand count too. That's an important difference from classic SEO.
There's no fixed timeline. After you roll out changes, search engines need to react, and your pages need to reach the top 10 of results. After that, LLMs still need to learn from the new data.
Since AI companies don't continuously retrain their current models, that learning feeds into future models rather than the current one. So you should expect noticeable changes only in the next generation of models, which tend to come out roughly every three months.
Yes, it does. To compete with large players, you need to focus on specialization and be open. LLMs favor specialists. For example, a large retailer might sell electronics broadly, while a small business might sell only headphones. So for questions about headphones, the LLM may well recommend you specifically.
Also, consider comparing yourself to the leader in your niche: if that leader can't describe on their site, in detail, how they process orders, what their internal turnaround times are between departments, or how they handle problem cases, but you can, the LLM will mention the giant and your small brand together, and explain why it included you as well.
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Journalist at Promodo
For over 6 years, I have been working as a journalist in the communications and marketing industry. I help brands communicate effectively through written content, engage with market experts, and create professional materials on topics related to business and marketing, sharing insights on working with marketing tools.
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