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Generative Engine Optimization: The Patterns Behind Ai Visibility

Generative engine optimization (GEO) is the practice of positioning your brand and content so that AI platforms like Google AI Overviews, ChatGPT, and Perplexity cite, recommend, or mention you when users search for answers.

If that sounds abstract, the results aren’t.

For bootstrapped form builder tool, Tally, ChatGPT became the #1 referral source.

They’re not alone. Across industries, the shift is already measurable.

ChatGPT reaches over 800 million weekly users. Google’s Gemini app has surpassed 750 million monthly users. And AI Overviews are appearing in at least 16% of all searches (significantly higher for comparison and high-intent queries).

The question isn’t whether AI is changing discovery. It’s whether your brand is showing up when it happens.

So GEO is real. But is it stable enough to invest in seriously?

That’s a fair question.

When we tracked 2,500 prompts across Google AI Mode and ChatGPT through the Semrush AI Visibility Index, the first thing we noticed was volatility.

Between 40 and 60% of cited sources change from month to month.

But underneath the variances, patterns emerged.

The brands showing up consistently shared specific structural characteristics. Entity clarity, content extractability, multi-platform presence made them easier for AI systems to find, trust, and reference.

In this guide, I’ll share what we’ve found about what GEO requires, how it differs from SEO, and the framework for increasing your visibility in AI-driven discovery.

What GEO Looks Like in Practice

GEO helps your brand appear in AI-generated answers.

For example, when someone asks an AI tool “What is the best whey protein powder for a mom in their 50s,” the response typically evaluates brands and recommends options based on ingredients, reviews, and credibility signals.

If your content or brand is included in that response, it’s an example of GEO in action.

Getting there requires coordinated effort across several areas:

Content strategy: Publishing information that AI systems can discover, understand, and extract for answers
Brand presence: Establishing your authority across platforms where AI tools pull information (not just your website)
Technical Optimization: Ensuring AI crawlers can access and process your content
Reputation Building: Earning mentions and associations that signal credibility to AI systems

These activities overlap with traditional SEO, but the emphasis shifts.

How GEO Differs from Traditional SEO

GEO builds on the same SEO fundamentals you already use. But it shifts the focus from rankings and clicks to how your brand is mentioned and cited inside AI-generated answers.

Here’s a snapshot of some key differences between GEO and traditional SEO:

What Changes	Traditional SEO	GEO
Primary goal	Rank in top search positions	Be referenced or mentioned in AI answers
Success metrics	Rankings, clicks, traffic	Citations, mentions, share of voice
How users find you	Click through to your site	AI includes you in generated responses
Key platforms	Google, Bing	Google AI Overviews and AI Mode, ChatGPT, Perplexity
How you optimize content	Title tags, keywords, site speed, content quality	Self-contained paragraphs, clear facts, structured data
How you build credibility	Backlinks, author credentials, reviews, domain authority	Positive mentions across trusted platforms and communities

Use this table to update your mental model.

Traditional SEO fundamentals still matter. We’re just adapting how we apply them as AI systems change how people discover information.

Now, let’s break down what this means in practice.

What Stays the Same

The core principles behind effective SEO still apply to GEO.

You still need to publish high-quality, authoritative content for real users. Your site still needs to be technically accessible. You still need credible signals of trust and expertise. And you still need to understand user intent and deliver clear value.

AI systems tend to reference content that is authoritative, well-structured, and easy to interpret. Those are the same qualities that support strong SEO performance.

If you already have a solid SEO foundation, GEO builds on it rather than replacing it.

Further reading: SEO vs. GEO, AEO, LLMO: What Marketers Need to Know

What Changes

Where GEO diverges is in how that foundation is applied.

1. Where You Need Presence

Traditional SEO focuses primarily on your owned properties, i.e. your website and blog.

GEO benefits from strategic presence across platforms where AI tools discover information, including:

Reddit threads where your target audience asks questions
YouTube videos demonstrating your expertise
Industry publications that establish your authority
Review sites where customers discuss solutions
Social platforms where conversations happen

2. How You Structure Information

AI systems extract specific passages from your content to construct answers. They pull a paragraph here, a statistic there, and weave them together.

This changes how you need to structure information.

When you’re explaining a concept, defining a term, or sharing data, that paragraph should ideally work on its own. AI systems often extract these substantive passages without the conversational setup around them. (We’ll cover the mechanics of how this works in the strategic framework later.)

You need clear headings to help AI identify which section answers which question.

Also, putting answers early in sections may make them easier for AI to find and extract.

Traditional SEO often rewards comprehensive coverage. GEO places more emphasis on content that’s easy to extract and reassemble. We’re still learning exactly how different AI systems prioritize structure, but clarity consistently helps.

3. What You Measure

Traditional SEO metrics like rankings, clicks, and bounce rate tell part of the story.

GEO adds new measurements, like:

AI visibility score: A benchmark of how often and where your brand appears in AI-generated answers
Share of voice: Your visibility compared to competitors in AI responses
Sentiment: Whether mentions are positive, neutral, or negative
Context or prompt: What questions or topics trigger mentions of your brand

Together, these metrics help you understand not just whether you’re visible, but how your brand is being positioned inside AI-generated responses.

You need both traditional SEO metrics and AI visibility metrics to understand your full organic search presence in 2026.

Note: You can track these metrics using Semrush’s Enterprise AIO, which monitors your brand’s visibility across AI platforms like ChatGPT, Google AI Mode, and Perplexity.

It provides granular tracking of mentions, sentiment, share of voice, and competitive benchmarking to help you optimize your AI visibility strategy.

5 Principles for AI Visibility: A Strategic Framework

An effective GEO strategy rests on five connected principles that work together to maximize your AI visibility.

(As AI systems evolve, specific patterns may shift, but these underlying principles provide a stable foundation.)

Each one addresses how AI systems discover, evaluate, and reference your brand.

Let’s look at them in detail.

1. SEO Fundamentals Are the Foundation

SEO fundamentals still matter for GEO, but for a different reason than in traditional search.

In AI-driven discovery, these fundamentals still function as optimization levers, but they influence retrieval, interpretation, and attribution rather than rankings alone.

They create the baseline conditions that allow AI systems to retrieve information, interpret it accurately, and attribute it to a source with confidence.

For instance, AI-generated answers are assembled from content that is accessible, readable, and attributable.

When accessibility, readability, or clear attribution are weak, even strong content becomes harder for AI systems to surface or reference reliably.

This is why many sources cited by AI platforms share characteristics long associated with solid SEO foundations.

The overlap exists because clarity and reliability still matter across discovery systems, even as the surfaces change.

Technical accessibility plays a role here.

Content that cannot be consistently crawled, indexed, or rendered introduces uncertainty at the retrieval layer.

Page performance has a similar effect. Slower or unstable experiences don’t block inclusion outright. But they reduce how dependable a source appears when answers are assembled.

JavaScript-heavy implementations highlight this dynamic.

Many AI crawlers still struggle to consistently process client-side rendered content, which can make core information harder to extract or interpret.

When that happens, AI systems have less certainty about using the content as a reference point.

But technical setup is only part of the equation.

AI systems also assess content quality and credibility. Information that reflects real experience, clear expertise, and identifiable authorship is easier to contextualize and trust.

Signals associated with E-E-A-T (Experience, Expertise, Authoritativeness, and Trust) influence not just whether content is referenced, but how it is framed within an answer.

Taken together, these foundations explain why SEO still underpins GEO. Not as a ranking system, but as the infrastructure that makes AI visibility possible.

Further reading: A technical SEO blueprint for GEO: Optimize for AI-powered search

2. Entity Clarity Shapes AI Understanding

Entities help AI systems understand and categorize information on the web. This includes distinguishing your brand from similar names, identifying what category you belong to, and understanding which topics you’re credible for.

AI systems don’t just read words. They interpret structure.

Before schema ever comes into play, they look for clear signals about:

What your brand is
What category it belongs to
What it offers
What it’s authoritative for

The most reliable way to provide those signals is through well-structured information.

If those signals are unclear or inconsistent, AI systems have less confidence when deciding whether and how to reference you.

Take monday.com as an example. When AI systems crawl websites and process information, they see “monday” mentioned in many different contexts.

Clear, consistent descriptions across the site and supporting sources help AI understand that monday.com refers to project management software. Not the day of the week.

The same principle applies to category clarity. If you sell organic dog food, AI needs to categorize your brand under pet nutrition, not general groceries or pet accessories.

When someone asks “what’s the best grain-free dog food,” AI is more likely to consider brands it can clearly place in the correct category.

On a product page, it should be unambiguous what each element represents — the product name, the description, the price, the attributes, availability and variants.

That clarity needs to exist in the visible page content first.

Schema markup can then mirror that structure in a machine-readable format (typically JSON-LD). And that same structured understanding should also be reflected in downstream systems, like your product feed submitted to Google Merchant Center.

In other words, the page structure, the schema markup, and the commerce feed should all describe the same thing in the same way.

The goal isn’t to “add schema.” The goal is to make your information logically structured so machines can consistently understand it across systems.

This is important because we don’t know how structured data is used inside large language models. Or how exactly schema influences training, retrieval, or real-time answer generation.

But we do know this: AI systems cross-reference signals from multiple sources and formats.

Your brand description on LinkedIn should align with what appears on your site. Profiles on Crunchbase, review platforms, or industry directories should reinforce the same category, positioning, and value proposition.

When these signals are consistent across sources, AI systems can categorize and reference your brand with greater confidence. When they conflict, confidence drops, and your brand is less likely to be mentioned.

This is why entity clarity isn’t just about a single markup tactic. It comes from designing your content and presence so machines can reliably understand who you are, what you offer, and where you belong wherever your brand appears.

Further reading: How Ecommerce Brands Actually Get Discovered In AI Search

Tip: You can check if your site has missing structured data that makes entity relationships unclear — along with other issues that could potentially be hurting your AI search visibility — using Semrush’s Site Audit.

3. Content Must Be Easy to Extract and Reuse

If entity clarity determines whether AI systems consider your content at all, extractability determines which specific parts get pulled into AI-generated answers.

This principle operates at the retrieval layer.

AI systems don’t consume pages the way humans do. When generating answers, they retrieve specific passages from across the web and assemble them into a response.

Here’s how it works mechanically:

LLMs break content into chunks, convert those chunks into numerical representations (vectors), and retrieve the most relevant passages when assembling an answer.

Those retrieved chunks are then synthesized into a response — often without the surrounding context from your original page.

This has practical implications.

Based on what we’ve observed, passages that retain meaning when read in isolation are more likely to be retrieved and used accurately. Passages that rely on conversational setup or references like “as mentioned above” or “this is why” tend to lose clarity when extracted.

Now this may not apply to every paragraph on a page.

But paragraphs that contain definitions, explanations, comparisons, or key facts should ideally stand on their own. These are the passages AI systems are most likely to extract without the surrounding narrative.

So what makes content extractable?

Self-contained paragraphs: Each paragraph expresses one complete idea that makes sense on its own, without vague references to surrounding text
Specific facts and statistics: Concrete numbers and clear statements are easier for AI to extract than vague generalizations
Clear, descriptive headings: Headings signal what each section covers, helping AI understand content organization
Front-loaded information: The main point appears at the start of paragraphs rather than at the end

One important distinction: This principle mainly applies to retrieval-augmented systems — like Google AI Mode and Perplexity with grounding, and ChatGPT with browsing enabled. These systems get content in real-time.

For base model knowledge (what the LLM learned during training), content structure is less important. That knowledge comes from training, not from retrieving per-query. Building presence in training data takes time and requires consistent, authoritative publishing.

Below is an example of self-contained content that AI systems can easily extract and reference.

It answers a single, well-defined question: which sources AI platforms rely on for finance-related queries
The main takeaway is stated immediately, without setup
Supporting context (platforms, percentages, category) is included within the same frame
The insight makes sense on its own, even if quoted or summarized elsewhere

The same extractability principle shows up in everyday writing as well.

For example, compare these two ways of explaining the same cooking technique:

Hard to extract: “There are several reasons this method works. After trying it, most people find their eggplant tastes better. That’s why many chefs use it.”

Easy to extract: “Salting eggplant for 15 minutes before cooking removes bitterness and excess moisture. This technique improves the final texture.”

Both explain the same idea. But the second version states the technique, timing, benefit, and result clearly, which makes it easy for AI to extract as a standalone passage.

Here are other examples:

When content is structured this way, AI systems can reliably retrieve relevant passages and include them in answers.

Over time, that increases the likelihood that your expertise is surfaced accurately when users ask questions related to your domain.

4. AI Visibility Extends Beyond Your Website

AI systems don’t just pull from your website when building answers. They gather information from YouTube, Reddit, review sites, industry publications, social platforms, and more.

This creates two opportunities for visibility:

(I) Your Owned Presence

Owned presence is content you or your team create on platforms beyond your website.

Your YouTube channel showing product features gives AI video content to reference
Your company’s participation in relevant subreddit discussions shows expertise in action
Your executives’ LinkedIn newsletters establish thought leadership

Podcasts, webinars, conference presentations, and educational platforms provide additional long-form content AI systems can extract from.

These platforms often play an important role in AI discovery.

In fact, Reddit, Linkedin, and YouTube were among the top cited sources by the top LLMs in October 2025.

When your brand creates valuable content on these platforms, you give AI systems more material to draw from.

But the key is creating substantive, helpful content that addresses real problems in your industry.

(II) Earned Mentions

Earned mentions are references to your brand that you don’t directly control.

Customer reviews on G2, Capterra, or Trustpilot describe real experiences with your product
Industry journalists mentioning your company in news articles provide third-party validation
Community discussions on Reddit or Quora where users recommend your solution show authentic sentiment. Like this:

When multiple independent sources discuss your brand in relevant contexts, AI systems have clearer signals to interpret your credibility.

Further reading: 7 ways to grow brand mentions, a key metric for AI Overviews visibility

Side note: Tools like Semrush’s AI PR Toolkit make this easier to evaluate at scale. Beyond counting earned mentions, it shows how your brand is framed across sources, including whether mentions skew positive, neutral, or negative.

This metric can be very important as you work to extend brand visibility beyond your website. Because sentiment influences how AI systems frame your brand in answers, not just whether they mention you at all.

Why Both Matter

Owned presence and earned mentions work together.

Your owned content demonstrates expertise and provides detailed information AI can reference. Earned mentions from customers and industry sources validate your credibility.

When AI systems encounter both, they build a comprehensive understanding of what you offer.

This owned and earned content may also become part of LLM training data in the future, shaping how AI systems learn about and reference your brand long-term.

5. Visibility Is Measured Differently in AI Search

Traditional SEO metrics (like rankings, clicks, and traffic) only tell part of the story. But they had one major advantage: the attribution path was clear.

A user clicked, landed on your site, and either converted or didn’t. You could tie that traffic directly to revenue.

AI search breaks that path. When an AI tool recommends your product to a user, they might never click through to your site. The conversion may still happen — they Google your brand name later, sign up the following week — but your analytics won’t connect it back to the AI mention that started it.

That’s the real measurement challenge. It’s not just that the metrics are different. It’s that the link between visibility and revenue becomes harder to trace.

The value here isn’t just the click. It’s being part of the answer.

This requires measuring your visibility differently.

Here are the key metrics to consider:

Citation frequency: This measures how often AI platforms mention your brand when answering questions
Share of voice: Your mention rate compared to competitors. If an AI answers 100 questions about “best CRM,” how many times do you appear vs. your rivals? This reveals your true competitive position.
Context tracking: Where do you appear? Understanding which specific prompts or topics trigger your brand mentions helps you identify the subjects you own versus where you’re invisible.
Sentiment: Are the mentions positive, neutral, or negative? A high share of voice means nothing if the AI is telling users your product is “overpriced” or “buggy.”

The challenge is that traditional analytics platforms (like GA4 or Google Search Console) cannot track these signals. They only see what happens after a click.

This creates a “measurement blind spot.” You might be the most mentioned brand in ChatGPT, but your standard dashboards would show zero activity.

Platforms like Semrush’s AI Visibility Toolkit are built to solve this specific problem. They help quantify these “invisible” GEO metrics, turning qualitative data (like sentiment and mention frequency) into trackable numbers.

Its Brand Performance report shows how visible your brand is in AI answers, how you compare to competitors, and whether mentions skew positive, neutral, or negative.

The toolkit also highlights AI visibility insights, helping you understand how your brand is currently interpreted in AI answers and where adjustments may improve visibility.

Ultimately, a modern search strategy requires monitoring two distinct dashboards:

One for your website’s performance (rankings and traffic) in traditional search. And one for your brand’s mentions across AI search

You need both to see the full picture.

What This Framework Doesn’t Guarantee

These principles increase your probability of appearing in AI answers. They don’t guarantee it.

The volatility in AI citations means even well-optimized brands experience fluctuation.

Different AI platforms weigh signals differently. User context and conversation history affect what gets cited. And AI systems are evolving rapidly — what works today may shift as models update.

Think of GEO like brand building: you’re increasing your odds across many moments of potential visibility, not securing a fixed position.

The brands that do this well show up more often, more accurately, and in better context. But there’s no “rank #1” equivalent to chase.

That realism isn’t a reason to ignore GEO. It’s a reason to approach it as an ongoing discipline. Showing up consistently, across surfaces, over time, is how you build trust with AI systems.

Frequently Asked Questions

What’s the biggest misconception about GEO right now?

The biggest misconception is that AI-generated answers are too volatile to optimize for.

While individual responses change, the underlying inputs do not. AI systems consistently rely on durable signals like authority, clarity, and trust. Brands with strong entity clarity and credible sources appear repeatedly, even as surface-level outputs fluctuate. The patterns are stable enough to act on.

Is GEO replacing SEO?

No, GEO builds on SEO fundamentals.

Traditional SEO optimizes for rankings and clicks. GEO optimizes for mentions, citations, and recommendations inside AI-generated answers.

They work together. Strong SEO creates the foundation (technical accessibility, quality content, credibility signals) that AI systems rely on when deciding which brands to reference.

How should we think about GEO in the bigger AI search shift?

The clearest way to frame it is as a hierarchy.

AI search is the environment
AI SEO is the practice
AI visibility is the outcome

GEO sits inside AI SEO as one way to improve visibility within generative systems. The goal is not optimizing for a single model or interface. The goal is being seen, trusted, and reused wherever people search for answers.

Further reading: How to Rank in AI Search (New Strategy & Framework)

What types of content are more likely to appear in generative AI responses?

Content that is easy for AI systems to retrieve, understand, and reuse is most likely to appear in generative AI responses.

In practice, this means clear, direct answers to specific questions, self-contained explanations, fact-based comparisons, and concise definitions that make sense without surrounding context. AI systems tend to pull individual passages, not entire pages, so structure and clarity matter more than length.

Does AI search favor large, well-known brands, or does GEO level the playing field?

Well-known brands often start with more authority, but they don’t automatically win. Smaller publishers can compete when they own a clearly defined topic, show up consistently across platforms, and are easy for AI systems to understand and trust.

In practice, focused niche sites may outperform larger brands when their expertise is clearer, better structured, and tightly aligned with specific audience needs.

What’s the right way to think about GEO moving forward?

The right way to think about GEO is as a long-term visibility discipline, not a short-term optimization tactic.

Success comes from making your expertise clear, consistent, and reusable wherever AI systems look for answers. That requires strong alignment across content, SEO, brand, PR, product, and customer touchpoints.

AI search does not change the goal of helping users. It raises the standard for coherence, accuracy, and trust across the entire web.

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