What is Generative Engine Optimization?

Generative Engine Optimization (GEO) is the practice of optimizing digital content so it can be accurately surfaced, cited, or referenced by generative AI systems such as ChatGPT, Google Gemini, Perplexity, and AI Overviews. Unlike traditional SEO, which focuses on ranking content in human-facing search results, GEO aligns with how large language models retrieve, evaluate, and present information during AI-generated responses.

The term was introduced in 2023 by researchers Gao et al., who developed the GEO‑Bench benchmark to study how different optimization techniques influence the visibility of content in generative outputs. Their findings showed that tailored GEO strategies could improve citation and visibility rates by as much as 40 %, underscoring its potential impact for publishers and brands.

As generative engines increasingly replace conventional search interfaces, GEO is becoming a critical discipline for ensuring content remains discoverable. It focuses on semantic clarity, factual accuracy, and the structured signals that increase the likelihood of being selected as a trusted source during inference. The objective is not merely to appear in a list of ranked links, but to become the underlying source that powers the answer itself.

This article explains how GEO works, how it differs from SEO, and why brands that act early stand to gain a lasting advantage.

SEO vs. GEO: How they differ and why both still matter

First of all, Search Engine Optimization is not dying. In fact, it remains one of the most consistent channels for long-term digital visibility. But the way people discover and consume content is shifting—and that shift is now large enough to matter. 

Industry research suggests that around 5% of global SEO traffic has already moved toward generative engines. At first glance that may seem marginal, but at internet scale, it represents billions of queries and a material change in how visibility is earned.

This shift is not a zero-sum competition between SEO and GEO, it is a response to the rise of new consumption habits. Instead of clicking through search results, users increasingly ask AI systems for answers and receive synthesized responses—often without visiting a website at all. Generative Engine Optimization (GEO), sometimes referred to as Answer Engine Optimization (AEO) or LLM Optimization (LLMO), has emerged to address this change.

Technically, SEO and GEO are built on different systems. SEO is optimized for traditional web crawlers—typically JavaScript-based bots that evaluate HTML content, metadata, backlinks, and site performance to determine relevance and rank. In this environment, your content earns visibility by matching search intent, gaining authority through backlinks, and being readable by structured crawlers. The relationship between effort and reward is straightforward: optimize well, get ranked, earn clicks.

GEO operates under a different paradigm. Visibility is determined not by crawlability, but by how well your content can be understood and retrieved by large language models during inference. LLMs do not follow traditional indexing paths. They rely on internal embeddings, semantic representations, training data snapshots, and real-time access to structured sources like Wikipedia, Reddit, forums, and selected publishers. As a result, your brand visibility is no longer a function of how well your content ranks, but how often it is cited, paraphrased, or referenced across the web.

The implications for brand strategy are significant. In traditional SEO, your content does the work for you. In GEO, your brand reputation, third-party mentions, factual clarity, and content usefulness all influence whether you are included in an AI-generated answer. Channels like LinkedIn, Reddit, Hacker News, and even product documentation are now part of the citation graph LLMs draw from—making dark social and user-driven discussion more influential than ever before.

There is also a qualitative shift in what content needs to do. Where SEO once encouraged high-volume publishing and keyword-dense articles, generative engines increasingly reward clarity, accuracy, and conciseness. Content that answers questions directly, supports claims with evidence, and provides meaningful perspective is more likely to be referenced by AI. As generative models become more selective, the surface area for visibility shrinks. Quality, not quantity, becomes the new baseline.

SEO still matters. It remains the foundation for web search visibility. But GEO is now equally essential for brands that want to maintain relevance as generative engines become the dominant interface for information. The smartest teams are learning to do both.

Why GEO matters

Here are a few reasons generative engine optimization matters.

You’re no longer optimizing for keywords—you’re being retrieved based on prompts

When users turn to generative engines like ChatGPT or Perplexity, they’re not typing in short phrases like “best CRM” or “employee engagement software”. They’re entering detailed, intent-rich prompts that often span full sentences or paragraphs. These prompts reflect not just what someone is looking for, but why they’re looking for it and what parameters matter most.

For example, a user might ask, “What’s the best compliance software for a financial firm with both U.S. and EU operations that supports automated audit trails and integrates with Workday?” That query has more complexity than any keyword tool could realistically model—and generative engines attempt to interpret and synthesize the answer in one pass.

This shift to prompt-driven search changes how content is matched to user intent. In traditional SEO, you can win traffic by targeting keywords that loosely match what people are searching for. In GEO, alignment has to be much tighter—because the engine is synthesizing a direct answer, not offering a menu of links.

If your brand is not clearly associated with the context, need, or decision criteria described in the prompt, you are unlikely to be surfaced. Generative engines are not just scanning for related terms; they are inferring meaning based on the full structure of the query.

To be visible, your content needs to reflect the exact problem the user is describing. That means covering real use cases in depth, clearly stating who your product is for, and offering specific answers that match the detail and nuance found in long-form prompts.

You only get one mention and it needs to work hard for you

Generative engines rarely return a list of ten results. Instead, they produce a single synthesized answer, drawing from what the model interprets to be the most relevant, credible, and contextually appropriate information. That answer may include one brand name. It may include none.

If your brand is mentioned, that moment carries weight. You don’t get a fallback listing if the description is weak, outdated, or inaccurate. You don’t get a second chance a few results down. Worse, the model may confuse your features with a competitor’s, misstate your value proposition, or pull outdated descriptions from third-party sources.

Consider a prompt like “best drag and drop design platform for small businesses.” You might reasonably expect Canva to appear—it’s widely known, well-funded, and broadly associated with ease of use. 

Yet across three major generative engines—Perplexity, Anthropic, and OpenAI—Canva is absent. Instead, tools like Hostinger, Squarespace, Wix, and Carrd rank highest. The sources cited include niche blog posts, comparison pages, and targeted small business guides. Canva isn’t mentioned in any of them.

This highlights a core truth about GEO: brand recognition does not guarantee generative visibility. What matters is whether your content ecosystem aligns tightly with the specific problem being described in the prompt. Brands with comprehensive, use-case-driven content that clearly speaks to narrow audiences—like “drag and drop for small businesses”—get surfaced. Those that lean on general brand equity alone often don’t.

In this environment, everything that gets said about your brand—on your website, in documentation, on third-party blogs, and across platforms like Reddit or LinkedIn—becomes part of the narrative generative engines assemble. Your content doesn’t just support discovery. It defines how your brand is interpreted, paraphrased, and positioned when the user isn’t there to judge for themselves.

GEO forces you to say what matters clearly, directly, and everywhere

For years, marketing and sales teams have tried to be “where the buyer is” and deliver the right message at the right stage. GEO collapses that entire journey into a single decision point: whether your brand is referenced when someone asks a relevant question.

That means your messaging has to be findable, consistent, and useful wherever it lives—on your site, in customer stories, in help docs, on partner pages, or discussed in Reddit threads and LinkedIn posts. If your differentiation is vague, if your content is spread thin across irrelevant topics, or if your claims aren’t backed by specifics, generative engines will pass you over.

On the other hand, if your value prop is expressed clearly in a way that aligns with buyer needs, GEO makes that messaging work harder than any campaign could. It puts your best positioning directly in front of high-intent users who may never reach your site otherwise.

You need to be discoverable now because GEO is growing fast

The shift from traditional SEO to GEO is already measurable. Around 5% of global search traffic has moved to generative platforms—and that share is increasing quickly. Based on usage trends and how platforms are integrating generative summaries by default, that number could reach 50–75% by 2030.

That’s not speculation. It’s backed by rigorous research from the GEO-bench benchmark, which found that visibility in generative responses can be improved by over 40% when content is optimized appropriately. And unlike traditional SEO, where traffic accrues over time, generative engine citations are decided instantly, based on content quality, clarity, and credibility at the moment of the query.

This means the time to act is now. GEO isn't something you layer on later. It’s the new baseline for visibility in AI-driven environments—and the earlier your content aligns with it, the more entrenched your brand will be as the go-to reference in your category.

How to improve your brand’s visibility in generative engines

Optimizing for generative engines requires a different set of priorities than traditional SEO. Instead of optimizing for web crawlers and link-based authority signals, you’re optimizing for large language models that retrieve and synthesize answers based on semantic understanding, factual consistency, and inferred trust. The following practices are emerging as key levers for increasing visibility in generative search environments.

Make your content semantically clear and structurally scannable

Generative engines rely on embeddings and token-level attention to interpret meaning. That means they prioritize clarity, structure, and internal consistency far more than human readers scanning visually. Models perform better when content is logically organized and easy to segment.

To improve semantic clarity:

• Use consistent H2 and H3 structures across long-form pages

• Break out key takeaways into bullet lists or Q&A sections

• Include “In summary” or “Key points” sections near conclusions

• Avoid dense paragraphs and redundant phrasing

• Make your use cases, differentiators, and outcomes explicit

These patterns help LLMs understand what your content is saying and who it’s relevant for. A clear semantic footprint improves the odds of being surfaced in response to high-intent prompts.

Add facts, quotes, data, and original thinking because LLMs cite what feels credible

One of the most effective ways to be referenced by generative engines is to give them something to quote. Models favor statements that are specific, supportable, and distinctive. Pages that include citations, original research, third-party quotes, or proprietary insights tend to get picked up more often because they contain information worth repeating.

This doesn’t mean stuffing your content with generic stats. Instead, include:

• First-party data, benchmarks, or survey results

• Quotations from internal SMEs or external thought leaders

• Case study excerpts and implementation results

• Direct citations (with links) to high-trust sources like Wikipedia, government sites, or industry reports

By grounding your content in verifiable, valuable detail, you increase its perceived reliability—both for users and for the generative engines responding to them.

Use metadata that generative crawlers can actually interpret

LLMs don’t crawl the web like traditional bots. They often rely on structured data, pretrained embeddings, and purpose-built ingestion pipelines to pull in new content. If your site lacks clear metadata or signals designed for LLM indexing, it’s easy to be skipped or misunderstood.

To improve ingestibility:

• Implement structured schema.org markup for all core content types

• Use the emerging llms.txt protocol to direct LLM crawlers to preferred pages

• Add AI-specific metadata in your HTML headers (e.g., ai-content, model-visibility, or inference-weight)

• Ensure content titles and summaries use plain language aligned with how real users phrase questions

This makes your content easier for LLMs to parse, attribute, and quote—especially in competitive categories where small edges matter.

Build third-party brand mentions—not just backlinks

In traditional SEO, backlinks signal domain authority. In GEO, what matters more is how often your brand is mentioned across trusted, model-accessible sources. Generative engines are heavily trained on Wikipedia, Reddit, news publishers, product directories, and large blogs. If your brand shows up consistently in those environments, you increase your chance of being retrieved as a relevant answer.

High-impact sources include:

• Wikipedia pages or citations (especially if accurate and well-referenced)

• Product comparisons on third-party blogs or YouTube

• Reddit threads and forums that include your name in authentic conversation

• Guest posts or feature coverage on PR-friendly media domains

LLMs reward brands with broad, credible surface area. Authority is now about recognition—not just link count.

Make sure your content is technically accessible to LLMs

Crawlability still matters—but not in the same way it did in early SEO. LLMs prioritize pages that are fast, lightweight, and structurally clean. Sites bloated with JavaScript or dependent on client-side rendering may not be parsed correctly, especially when inference systems pull content dynamically.

To improve technical accessibility:

• Avoid client-side rendering where possible; prefer server-rendered or static pages

• Minimize JavaScript dependencies and excessive third-party scripts

• Optimize for mobile performance and clean CSS

• Ensure fast load times (ideally under 2 seconds on mobile)

• Use descriptive, text-based navigation instead of complex UI elements

These fixes make your content more accessible not just to users, but to the systems responsible for generating answers.

Track generative visibility with purpose-built tools

Unlike SEO, where you can measure rankings and traffic using standardized analytics, GEO is still emerging—so visibility must be monitored using newer tools that focus on generative citation and retrieval.

Some early tools to monitor LLM visibility include:

• HubSpot AI Search Grader: Evaluates AI readiness of blog and landing pages

• Probe Analytics: Shows how you show up across generative engines, and how your competitors stack up.

Tracking performance helps you adjust content strategy, monitor brand representation, and measure whether your efforts are actually resulting in citations.

How to use Probe Analytics to execute your GEO strategy

We built Probe to be the operating system for GEO: not just a tracker of brand mentions, but a diagnostic tool for AI visibility. It helps you see what generative engines are saying about you, what they’re getting wrong, and how to influence the answer layer before it locks in.

Here’s how teams are using Probe today to shift perception, outrank competitors, and drive results from AI search.

Diagnose how you’re positioned in LLM answers—prompt by prompt

Traditional SEO tools tell you how your site performs in Google. Probe tells you how your brand performs in answers.

Every day, Probe runs a set of tracked prompts—questions your buyers are actually asking—across major LLMs like ChatGPT, Claude, Gemini, and Perplexity. For each prompt, you’ll see:

• Whether your brand was mentioned

• Your rank and visibility share across models

• The exact language used to describe your offering

• Which competitors were included instead

• Whether the framing was neutral, favorable, or incorrect

It shows you, in black and white, how generative engines are shaping buyer perception. You don’t have to wonder if your product messaging is landing or if your features are being surfaced. You can see what prompt phrasing triggers your inclusion, where you’re absent, and how to fix the gaps.

It’s like running focus groups across LLMs every day—and getting structured, model-level feedback in return.

Uncover why competitors are being favored and how to close the gap

GEO isn't just about whether you show up, it's about why others do. Many teams assume that AI rankings are based on domain authority or traffic—but in practice, models surface content based on how closely it matches the semantic intent of a prompt.

Probe gives you a lens into that decision-making layer.

• You’ll see which competitors are consistently mentioned across prompts

• You can compare average rank, citation volume, and framing sentiment

• You’ll know exactly which third-party sites or assets are lifting your competitors into AI-generated answers

This is citation intelligence—what content, where, is fueling LLM responses. If a blog post on “Top Talent Marketplace Platforms” keeps getting cited, you’ll know. If your content is being skipped in favor of a legacy comparison table from 2021, you’ll see that too.

Armed with this, you can act:

• Rework existing content to better match high-performing prompt phrasing

• Fill content gaps with comparison pages, use-case guides, or reviews

• Push citations to strategic publishers, not just high-domain ones

In GEO, influence is about diagnosing what works and then deploying content that maps precisely to those mechanics.

Monitor changes in visibility and reputation over time

Generative rankings are dynamic. You might win a prompt one week and disappear the next. Probe tracks this volatility—so you can see exactly when your share of voice drops, your position slips, or your product gets rephrased in a way that undercuts your value.

With Probe, you’ll get:

• Daily prompt tracking across models

• Historical trendlines for rank, sentiment, and inclusion

• Alerts when you’re added, removed, or misrepresented

This lets you correlate shifts in visibility with real-world changes—like launching a new landing page, issuing a press release, or updating your product description. You’ll know whether your GEO efforts are working, and where to double down.

More importantly, it helps you get ahead of reputation drift. If an outdated product description is being lifted from a third-party forum, you can fix it before it calcifies in model memory.

Tie LLM visibility to real-world traffic and behavior

Most teams can’t answer a simple question: Is AI search actually driving traffic?

Probe closes that loop.

It shows you:

• Which AI engines are sending sessions to your site

• What percentage of your total traffic comes from AI referrals

• Which URLs are being cited—and which are actually receiving visits

It’s one thing to be mentioned in ChatGPT. It’s another for that mention to lead to a session, a click, a conversion. With Probe, you’ll know the difference.

Act on high-signal prompts before competitors do

One of the hardest parts of GEO is knowing where to focus. With tens of thousands of potential prompt variations, how do you decide which ones to optimize for?

Probe simplifies this with a prompt intelligence engine that suggests high-signal prompts based on:

• Existing mentions across LLMs

• Common competitive comparisons

• Relevant category and intent signals

You can accept or reject each one with a click—and immediately begin tracking performance across engines.

This ensures your strategy is grounded in real demand, not hypothetical keyword lists. You’re optimizing for what people are already asking—and what LLMs are actively answering.

And because visibility in LLMs compounds over time (as models favor consistent, trusted sources), early movers lock in reputational advantage. Probe gives you the radar to spot those openings—and the tools to act before competitors catch on.