Your CEO just announced that it's time to go all in on GEO, and you enthusiastically nod along. Except you (and literally everyone else) have no idea how to execute on it. But if you're a marketer who's been watching AI steamroll search results, you at least know your old SEO tricks aren't cutting it anymore.
These days, when someone searches for information, they're increasingly likely to get an AI-generated answer directly at the top of the results, summarizing information from various sources without you ever having to click on anything. The practice of optimizing for these results is called generative engine optimization, or GEO (which sounds more like something a mechanic would try to upsell when you get an oil change).
As someone who's been doing SEO since way before AI learned to lie convincingly, I can tell you that the fundamentals are still there (create good content), but there are plenty of things to keep in mind that are different for the AI bots.
Here, I'll go over the basics of GEO and how it differs from SEO, and I'll share my approach to optimizing for generative AI.
Table of contents:
What is generative engine optimization?
Generative engine optimization is the process of optimizing content to boost its visibility in generative AI platforms like Google's AI Overviews and AI Mode, ChatGPT, Claude, Perplexity, and other LLM-based engines.
Unlike search engine optimization (SEO), which is designed for ranking high in search engines through keyword usage, backlinks, and technical site optimization, GEO search prioritizes factual, high-authority, and semantically rich content that AI engines can easily summarize or cite when answering user questions.
The way GEO works is surprisingly simple (which, of course, means it's actually very complicated). AI search engines, or generative engines, don't just serve you a list of links like search engines of yore—they actually read through websites, understand the content, and then generate a new, synthesized answer based on what they've found.
The end goal of GEO is to teach AIs to recognize, trust, and recommend your brand, so that when someone asks ChatGPT, "What's the best Slanket alternative?" or "How do I make gluten-free pasta?" or "Why does my dog snuffle my face when I'm asleep?" (seriously, why does he do that?), your content or your brand name is what it chooses to reference in its answer.
GEO vs. SEO
Classic SEO and GEO are two sides of the same coin. While they share DNA, they're different beasts. Let me break down what sets them apart and why you need both.
| Traditional SEO | GEO |
|---|---|---|
Focus | Ranking pages on Google | Getting cited in AI answers |
Content format | Complete web pages | Modular answer chunks |
Method | Keywords and backlinks | Context and citations |
User interaction | Click through to websites | Read answer directly |
Performance metrics | Traffic and rankings | Reference rate/brand mentions |
Goal | Drive website visits | Influence AI responses |
Focus
With SEO, you're chasing the top spot for your keywords, and every little tweak is meant to push you higher up the page. You've won when you dominate the search engine results page (SERP).
GEO is about becoming the answer itself. AI doesn't care if you mention "email marketing" 15 times in an article about email marketing best practices. It cares if you explain concepts clearly and provide actionable advice that it can steal from you and regurgitate.
Content format
For SEO optimization, we've spent years focusing on text-first content with the right keywords, meta descriptions, headings, and backlinks.
But generative engines are increasingly multimodal, meaning they can understand and incorporate images, videos, and audio into their responses. So for GEO, you need content that's adaptable across different formats because the AI wants to see your content from every angle, like a very discerning art critic.
Method
SEO relies heavily on keyword stuffing and backlinks. Do your research, weave in the terms, and get other websites to link to you. But what used to be considered a standard practice is now weird and off-putting, like calling someone just to talk or clown decor in children's bedrooms.
GEO emphasizes creating comprehensive, well-structured content that directly addresses user questions. Suddenly, all the old tricks feel cheap. ChatGPT doesn't give a hoot about your keyword density.
User interaction
SEO requires clicks. Users see results, read descriptions, click links, and travel to websites. With generative engines, the AI provides answers directly, often with sources, and users get what they need without the cardio of clicking.
Ironically, content succeeds by keeping people away from it, which can be terrifying for websites that rely on traffic for ad revenue or lead generation. If users get answers without clicking, how do we drive traffic? (This used to keep me up at night, along with my dog's face snuffling.)
The silver lining is that the traffic you do get from AI engines is often higher quality. When someone clicks through from a generative engine, it's usually because your content tightly matches their search intent and they want more than just a surface-level summary.
I've seen this with my own content. Articles cited by AI get fewer immediate clicks but convert at a significantly higher rate since users have already been pre-sold by the AI's summary.
Performance metrics
Most know the SEO scorecard by heart: rankings, traffic, conversions. It's all there in Google Analytics.
GEO metrics feel like the Wild West. We're asking new questions: how often are we mentioned? What's our share of voice in responses? Do citations drive brand searches? The tools to track this are literally being invented as we speak.
My agency measures both now—traditional metrics for SEO health and citation tracking for AI influence.
Goal
With SEO, the entire goal is to get the click. Everything funnels back to driving traffic to your website.
With GEO, the goal is explicit citation and inclusion in AI search results, which can lead to increased visibility, brand awareness, and potential backlinks. It's like becoming famous, except nobody knows who you are, you don't get any money, and a computer is taking all the credit for your work. Actually, it's exactly like being a content marketer.
How generative AI engines work
Generative AI engines operate on a completely different principle from classic search engines. Instead of just indexing and ranking web pages, they have complex neural networks trained on crazy amounts of data, allowing them to understand and generate human-like text.
Training data: Generative AI models are trained on massive datasets like books, articles, code, images, and the collected ramblings of the internet. It's the equivalent of binge-reading the entire "Library of Babel," except the goal isn't enlightenment so much as the ability to convincingly regurgitate.
Neural networks: Aside from being an epic band name, neural networks are computer algorithms designed to function like a human brain. (And by "function," I mean simulate functioning, in the same way a wax museum simulates celebrity—with eerie accuracy and none of the warmth.) Neural networks have layers of artificial neurons that process information, with each layer learning progressively more abstract features from the data.
Learning patterns: The neural networks learn by modifying the connections between neurons based on the data they've seen before. This allows the AI to identify patterns, semantic connections, and stylistic cues until it can confidently assert that the phrase "the quick brown fox" is most likely followed by "jumps over the lazy dog," and not, say, "joined an ashram in Rishikesh."
Response generation: Once trained, the model generates new content by taking a user prompt and using its learned patterns to predict the next most statistically appropriate response. For text generation, this involves predicting the next word in a sentence. For images, it entails conjuring a new image from a description like a sketch artist at a seance.
Retrieval-augmented generation (RAG): Most models you interact with, like Perplexity or ChatGPT search, supplement their "memory" with live, real-world information plucked from the internet. This is where SEO still matters—these systems are more likely to cite and reference high-ranking articles, then use generative AI to summarize or synthesize that information. So while keyword stuffing won't impress ChatGPT, having strong domain authority and search rankings absolutely will.
Generative engine optimization benefits
My agency has implemented GEO strategies for dozens of clients already, and we've seen concrete benefits that go beyond typical SEO wins. Here's why GEO matters:
Enhanced user experience and personalization: Generative engines provide immediate, conversational answers. When your content is optimized for these platforms, users get high-quality, accurate information faster and with less effort.
Competitive advantage and market leadership: Right now, GEO is still in its early stages, which means there's a first-mover advantage for brands that adapt quickly. It's like bringing a fire hose to what everyone else thinks is still a water balloon fight. It gives you a massive advantage, and everyone's going to be pretty upset when they figure out what you're doing.
Brand authority and credibility: Being featured in AI responses builds trust and strengthens your reputation, which can lead to more direct searches for your brand and higher conversion rates.
Improved content quality and relevance: Many of the optimization strategies for GEO actually make your content better overall. This often leads to better engagement metrics even outside of AI search, as users find your content more helpful and relevant.
New performance metrics and data insights: GEO introduces new ways to measure success, like how often your content is cited in AI responses (reference rate). Tracking these metrics can reveal patterns and opportunities you might miss with traditional analytics.
How to optimize for GEO
I won't lie to you—a lot of this will sound familiar if you've been doing SEO for a while. That's because good content practices are still good content practices, regardless of whether a human or an AI is reading your stuff.
Also, we don't know everything about the specific nuances of optimizing for generative engines yet. Honestly, not even the people who built the generative engines know the specific nuances. But here's what we do know.
1. Answer questions clearly and directly
If there's one thing AI engines love more than devouring all of humanity's knowledge and repeating it with varying levels of accuracy, it's content that gets to the point.
AI looks for immediate, concise answers to user queries. If someone asks, "How do I make ribollita?" they don't want to hear about your summer in Tuscany, where you discovered your passion for cooking after a torrid affair with a local chef named Giuseppe. Just tell them how to make the dang soup.
Start with an answer that's short, accurate, and immediately useful. Then explain why it's the answer. It's like spoiling the ending of a movie in the first sentence, but that's what the robots like.
Generative models are trained to understand natural language and tend to favor content that flows like human speech. When I write an article, I try to imagine I'm explaining the topic to a smart friend who knows nothing about it. This friend asks good follow-up questions and appreciates when I back up my claims with actual evidence and an occasional joke. This low-stakes TED Talk with an imaginary companion helps produce content that appeals to both humans and the watchful gaze of AI.
2. Use structured formatting
Structured content is easier for AI algorithms to understand, interpret, and prioritize. They don't have time to sift through your stream-of-consciousness rambling (unlike my therapist, who is contractually obligated to listen to me for a full 50 minutes).
Break information into digestible chunks and liberally use formatting that makes content easy to scan and extract:
H2 for primary sections
H3 for supporting subtopics
Bullet points for multiple items or features
Numbered lists for sequential steps or processes
Tables for comparisons or structured data
Bold text for key points or terminology
FAQs for common queries
Each section should be able to stand on its own, like an Edwardian orphan with good posture. AI might extract just one paragraph, so make sure it makes sense in isolation.
Headers should promise what each section delivers. If your H2 says "Why Choose Our Product," then the subsequent text shouldn't be a meandering essay about your childhood gerbil. It should explain why someone might choose your product. Also, make URL slugs descriptive: /how-to-choose-crm-software beats /archive/post-123.
Implement schema markup to give AI extra context about your content's purpose. Add FAQPage schema for question pages, HowTo schema for tutorials, or Article schema if you're pretending your blog post is journalism.
3. Include authoritative proof and concrete facts
Remember how your mom would never believe you were sick unless you had a fever of at least 100.4? AI is your mom now. It wants evidence before it decides your content is worth citing.
To increase trust and visibility, support every claim with concrete proof:
Cite statistics from credible, third-party sources
Quote subject matter experts directly
Link to original research (not just blogs summarizing it)
Include case studies or firsthand data
Use real numbers—percentages, dollar amounts, dates, benchmarks
This is basically how I argue with my partner—the more specific facts I can cite about who left dishes in the sink and for how many days, the more likely I am to win the argument. (Except I somehow never win the argument.)
4. Optimize for semantic search, not just keywords
When I yell "WHERE ARE MY KEYS?" as I'm running out the door, I'm not just asking for their literal location. I'm expressing frustration, time pressure, and implying that someone might have moved them. This is similar to how semantic search is getting better at understanding meaning rather than just words.
Identify long-tail, question-based, and conversational keywords that mirror how people ask questions. Folks don't ask AI for "pizza recipe." They ask, "How do I make pizza that won't make me feel guilty when I inevitably eat the whole thing?" Your content should answer both.
Generative engines use NLP techniques like named entity recognition (classifying named objects into predefined categories like person, organization, locations, etc.) and relation extraction (identifying semantic relationships between entities) to verify associations between places, products, and things. Using semantically connected entities in your content helps the model validate its relevance and confidently include it in synthesized answers.
For example, if you're writing about Tesla, you may mention Elon Musk, electric vehicles, autonomous driving, and billionaire feuds. A generative engine can use these entity connections to infer that your content is about the car company, and not the Serbian-American inventor with a complicated relationship to pigeons.
Tip: Search your target query in AIO panels and AI chatbots and note which sources they cite, what format they use (paragraphs, lists, haiku, etc.), and how they structure their answers. Then reverse-engineer it to map generative search intent. I'll often ask different models the same questions just to see how their answers differ.
5. Boost credibility with freshness
Just like dating profiles, generative models prefer content that's up to date, honest, and doesn't hide its credentials under a pile of bathroom selfies and lies about height.
Outdated information can reduce your chances of being featured in AI responses, especially for topics where recency matters. If your last blog post was in 2019 and cites Yahoo Answers, it's going to get skipped.
We update old content yearly (sometimes more) to:
Check that statistics are current
Update tool features and pricing
Add new examples
Remove outdated references
Update the "last updated" date
6. Build topical authority
To truly dazzle the algorithm, embrace E-E-A-T, or experience, expertise, authoritativeness, and trustworthiness. I can't emphasize this enough—one can't simply declare oneself an expert and expect the world (or worse, the internet) to vigorously nod in approval.
AI wants to see the receipts. This means showcasing your team's actual lived experience and backing it up with author bios, case studies, testimonials, and a fleshed-out "about" page.
7. Use off-site marketing for brand recognition
Then, there's the matter of authority. And I don't mean the sort handed out by dubious LinkedIn courses, but the kind you earn by becoming annoyingly indispensable.
Maintain consistent messaging by using the same brand voice on every channel, and make sure your value propositions are uniform across all online platforms. This consistency acts as a breadcrumb trail for AI systems trying to figure out whether your brand is a trustworthy entity or a content farm run by raccoons.
And don't stop at the off-site channels you vaguely control, like social media; make sure other folks are talking about you too. Take Zapier, for example. It's one thing for us to say we're the best AI orchestration platform. But when dozens of reputable sources independently say it, AI has concrete evidence to treat it as the "correct" answer.
Future-proof your content strategy with AI orchestration
Nobody has a perfect playbook for generative engine optimization yet, but early movers are already pulling ahead by automating their GEO strategy. Instead of getting bogged down in repetitive tasks, use AI orchestration to handle the repetitive parts of optimization, like reformatting content for different platforms, tracking brand mentions across AI engines, and maintaining content freshness at scale.
Using Zapier to automate your GEO workflow helps you stay consistent without burning out your team—and it can help you unlock new GEO marketing possibilities. The key is connecting your content tools with the rest of your marketing stack so you can create, optimize, and track performance throughout the process.
Related reading:
This article was originally published in February 2025 by Dmitry Dragilev, founder of TopicRanker. The most recent update was in July 2025.
