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A team can publish strong, useful articles and still see them ignored by systems like ChatGPT or Google AI Overviews. The reason is straightforward. In classic search, visibility often came from keywords and relevance signals. In generative answers, what gets reused is what is easy to extract as meaning: facts, numbers, clear conclusions, and a readable structure.
That is what GEO is about. It is the work of packaging content so generative systems can use it with confidence. In this guide, Inna Sidorenko, Senior SEO at Why SEO Serious, explains which formats these systems prefer and what to adjust so content shows up in answers and gets cited.
What GEO is and how it differs from SEO: the new reality of search
Imagine a query that is not “buy running shoes in London,” but: “Recommend comfortable road running shoes for flat feet.”
A classic crawler, the main “customer” of SEO for decades, helps less with prompts like this. It can find pages by words and relevance signals. It does not assemble a single, coherent answer from multiple sources.
GEO focuses on the layer that produces the answer. This layer includes AI assistants like ChatGPT and AI answers inside search, such as Google AI Overviews. These systems choose sources they consider reliable, then extract facts, data, and recommendations to build the response.
The key difference:
| Classic SEO | GEO |
|---|---|
|
A competition for a ranking position. The goal is for a crawler to find a page, interpret relevance, and place the page in the top results. The main prize is the click. |
A competition for being treated as a source. The goal is to show that the content is trustworthy and easy to parse. Then a system can reuse parts of it in an answer, and sometimes cite it. The main prize is being included in the response itself. |
A simple analogy:
- SEO is a storefront on a busy street, meaning the search results. The job is to get noticed and earn a visit.
- GEO is supplying information to a guide on that street. When a person asks for advice, the guide uses sources it trusts and turns them into an answer.
One important point: GEO builds on SEO.
Technical SEO is still the foundation: site speed, crawl accessibility, and clean structure. If a site is slow, overloaded with JavaScript, or blocked from indexing, systems may not be able to read and evaluate it at all.
👉 Before creating content for generative answers, it helps to change the framing. Think less about keywords. Focus on the expert question a page answers, and how clearly that answer is structured.
What types of AI platforms exist, and how to optimise for them
The world of generative AI is not uniform. User queries can be handled by different systems. Each system has its own priorities and trusts different data sources.
For these platforms to pay attention to your content, it needs to meet specific criteria. Broadly, you can group them into two categories: major search engines with built-in AI, and specialised AI systems.
Major search engines: where your main audience is
Here, the rules are set by large search platforms. Their AI is tightly connected to classic search and their own ecosystems.
- Google AI Overviews pulls information from across the web, but with its own priorities. Research suggests its Gemini model considers mentions on professional networks (LinkedIn), platforms such as YouTube, Reddit, and Quora, and signals like awards and inclusion in reputable lists. For local businesses, a well-maintained Google Business Profile remains critical.
- Copilot Search in Bing relies heavily on Bing’s own search experience and Microsoft surfaces. It produces a curated answer and shows cited sources, so the system needs content it can crawl, trust, and referencу. A verified listing in Bing Places for Business (Bing Search and Bing Maps) and an accurate product feed via Microsoft Merchant Center are direct signals that help the system connect your site, your business entity, and your offerings.
Specialised AI systems: how to become a source, not just a destination
These platforms act as independent decision-makers. A user can get a complete answer without visiting websites. Your goal is to become a trusted data source for them.
- Perplexity works as an answer engine for research. It operates in real time and consistently cites sources. Almost half of its references come from Reddit, which suggests a preference for live discussions and community opinions. To get on its radar, you need expert visibility in relevant forums and on platforms like Quora or Yelp.
- ChatGPT can use Bing for browsing, but it has its own preferences. Almost 50% of its citations come from Wikipedia, and a large share also comes from Reddit. This points to a pull toward structured, canonical sources and active discussions. In practice, the strategy often includes building a Wikipedia presence where appropriate and taking part in expert discussions on relevant forums.
- Voice assistants handle a different kind of query: conversational, with natural phrasing. Optimising for them often means answering the question directly in the first paragraph, using more conversational wording, and adding clear local signals for businesses with physical locations.
Factors that influence whether content appears in AI answers
Optimising for generative AI systems is not limited to picking keywords. These systems assess content and its sources using qualitative signals that can be shaped. This includes E-E-A-T (experience, expertise, authoritativeness, trust) and digital activity.
E-E-A-T
AI systems are trained to prioritise information from verified experts, not random authors. Algorithms analyse not only the text but also its context: who wrote it, how trustworthy the source is, and which other authoritative sites reference it.
How to implement this in practice:
- Make experts visible. Each article, especially in expert blogs and in areas like healthcare, finance, or law, should name a real author and link to a profile (full name, role, photo, social links, education, experience). A required technical step is to mark up author information using schema.org Person or Author (schema.org).
- Use first-person experience signals. Content grounded in real experience (“In this article, as an architect with 10 years of practice, I will break down…”) is rated higher than generic, impersonal theory.
- Build a digital reputation beyond the site. Work so the brand is mentioned in a consistent context on authoritative resources (industry outlets, media, review sites). When an AI system repeatedly sees a stable association like “Brand X = a reliable builder” across different sources, it starts to treat it as a fact.
Activity and sharing
For an AI system, silence around your article is a signal that it is not relevant. Active discussion and distribution create engagement signals.
How to build an effective distribution flow (B2B example):
- Publish a deep research piece on an industry platform or a respected trade publication.
- Share the key points in relevant industry newsletters and social posts.
- Discuss practical takeaways in professional communities (for example, LinkedIn Groups, Slack or Discord communities).
- Create an adapted version for a broader audience on platforms like Medium.
Discussions in relevant communities matter more than sharing for the sake of sharing.
Structure and clarity
If E-E-A-T and activity answer “why trust this,” content structure answers “how to extract it.” AI systems need to grasp the point of a text immediately.
- Heading hierarchy (H1–H4) as an AI map. A clear hierarchy turns an article into a schema that a machine can follow.
- Active use of lists and tables. In this format, facts are extracted with maximum precision.
- Dedicated meaning blocks. Put conclusions, instructions, or “common mistakes” analyses into separate blocks. This signals higher importance.
- An FAQ block is ideal for AI dialogue. It is a ready set of question–answer pairs. For best results, add
FAQPagemarkup (schema.org).
This approach turns your content into a source of facts that is easy for machines to analyse.
Conclusion: GEO is a continuation of SEO
Optimising for generative AI does not replace SEO. Search still tries to choose the best answer: useful and reliable.
What has changed is the “language” we use to communicate with algorithms. We used to speak in keywords. Now the conversation is about expertise, structure, and digital reputation.
The next step usually does not require starting from scratch. It is better to look at your current SEO foundation from a new angle and run an audit.
You can trust this to professionals
Technical work often eats up time and blocks progress on the substance. We can take it on: we will check speed, indexing, rendering, and structure, then deliver a clear, prioritised list of fixes.
Three things worth checking.
- Trust. The author is clearly identified, their expertise is easy to verify, and the page includes proof of real experience and quality.
- Accessibility. The page is easy to crawl and parse, and the content is broken into clear meaning blocks that a system can extract.
- Relevance. The content is published in the right place, and the format matches what AI platforms tend to reuse and what real audiences actually consume.
GEO makes a strong technical foundation the required starting point for moving to meaning-level optimisation. When the basics are solid and these three pillars are in place, content works in two modes. It ranks in organic results and appears more often in generative answers. The outcome is not only the click, but also a place inside the recommendation itself.
