If you’re leading marketing at a high-growth B2B company right now, you’ve probably asked some version of this: “Do we need to rethink our SEO strategy because of AI?”
Or maybe:
“What’s the real difference between ranking in Google and showing up in ChatGPT?”
Fair questions.
But let’s be honest. The answers floating around online are a mix of “SEO is dead,” “just use better prompts,” or “don’t worry, it’s all the same.”
None of that’s helpful. And none of it respects what you actually need to make smart decisions.
Here’s what’s true: SEO is still very much alive. But the way people find, compare, and trust information has changed. Fast. And permanently.
The rise of large language models didn’t kill SEO. It introduced a second game.
Traditional search engines retrieve and rank content. Generative engines synthesize and respond. That difference in how they work fundamentally changes how you show up, how you get mentioned, and how you get chosen.
If you only understand one system, you’re operating with half a playbook.
This guide breaks down how each one works, why they’re different, and what it means for your strategy. Not from a hype-driven “AI is taking over” angle. From a place of clarity. From someone who has spent years optimizing for both search results and AI visibility and knows they require different moves.
My goal isn’t to convince you that GEO replaces SEO. It doesn’t.
My goal is to help you stop guessing, start seeing the whole picture, and build a content strategy that earns visibility wherever your buyers are looking. Whether it’s page one of Google or the top of a Perplexity summary.
For the impatient among you here’s the high level.
GEO Vs SEO TL;DR
Everything you want in a nice tidy table. I know. I love it too.
| Component | SEO Focus (Rank) | GEO Focus (Be Cited) |
|---|---|---|
| Content | Keyword-targeted blogs, pillar pages, guides mapped to user intent | AI-ready formats: best-of lists, Versus pages, FAQs, stats-rich reports |
| On‑Page / Structure | Clear H tags, title tags, schema for featured snippets | FAQ schema, TL;DR summaries, author bylines and citation formatting |
| Off‑Site Authority | Backlinks from trusted domains like blogs, media sites, directories | Mentions in roundups, forums, Wikipedia or review sites LLMs pull from |
| Technical Setup | Fast-loading site, mobile-first, clear sitemap and canonical tags | Schema markup, llms.txt, AI-friendly crawl structure and semantic clarity |
| Measurement | Keyword positions, organic sessions, backlinks reports | Prompt presence, citation counts, share of AI voice, zero-click visibility |
| Ultimate Goal | Earn clicks via rankings | Be part of the answer LLMs deliver |
Got it?
Fantastic.
Let’s break down how each system actually works so you can make smarter calls with your strategy.
We’ll start with traditional search engines.
How Traditional Search Engines Work
Let’s keep it simple.
Traditional search engines like Google and Bing don’t go out and “find” content when someone types in a search. They already have it. What they’re really doing is pulling from a massive index they’ve been building nonstop for decades.
Here’s how it works at a high level:
Crawling
Search bots visit your website. They read your content, look at your links, and follow paths from one page to the next. This is how they discover new content and check for changes.
Indexing
Once crawled, your page gets stored in a database. That database contains trillions of pages. It’s organized based on the content itself, its structure, how it relates to other pages, and a mix of contextual signals the engine collects.
Ranking
When someone searches, the engine runs that query through its algorithms to figure out which indexed pages seem most relevant and trustworthy. It weighs factors like content quality, on-page signals, backlinks, usability, and historical performance. Then it displays those results as a list of clickable links.
You publish content. The engine decides how much of it to crawl. Then it decides if any of it deserves to be seen.
It’s not personal. It’s just a system that rewards clarity, technical hygiene, and relevance. Still extremely powerful. Still how most people find answers every day. And for a lot of categories, still your best source of consistent pipeline.
What’s important to understand is that this system is retrieval first. It finds documents that already exist and sorts them based on how well they match the query.
That’s the key distinction. Traditional search finds and ranks. Generative AI synthesizes and responds. And that shift changes everything about how content gets discovered.
How Generative AI Engines Work
If traditional search is built on retrieval, generative AI engines are built on prediction.
Instead of returning a list of documents, generative engines like ChatGPT, Claude, Gemini, and Perplexity create an answer in real time.
That answer is generated based on everything the model has already learned during training, combined with any current data it can access through retrieval.
Here’s how it works at a high level:
1. Pre-training
Large language models (LLMs) are trained on massive amounts of publicly available and licensed text. This includes books, blogs, forums, documentation, and more. The goal isn’t memorization. It’s learning the patterns and structure of language to predict the next word in a sentence with high accuracy.
2. Fine-tuning and instruction alignment
After pre-training, the model is fine-tuned to better follow human instructions. This is what helps it respond to questions, write helpful answers, and avoid saying things it shouldn’t.
3. Retrieval (when enabled)
Some models are given tools to pull in current information from the web or proprietary datasets. This retrieval layer lets them ground their responses in fresh or more accurate information. Perplexity and Google’s AI Overviews are examples of this hybrid approach.
4. Generation
When someone asks a question, the model processes it, uses everything it has seen or retrieved, and generates a response one token at a time. The output is based on probability, not rankings.
Here’s the important part. LLMs don’t just pull content from your website like a search engine. They synthesize an answer based on prior training data, citations they’ve picked up, and what their retrieval tool can access if enabled.
If your brand shows up, it’s because the model has seen your name in trusted, relevant contexts and decided you belong in the conversation.
Just because your blog ranks well in Google doesn’t mean you’ll be mentioned in a Claude answer. That’s a different system with different rules.
For example, if someone asks “What are the best platforms for B2B SEO?”, the model might mention platforms it has seen discussed frequently, cited in high-authority sources, or included in detailed comparisons. If your content wasn’t there, or wasn’t structured in a way that helped the model understand it, you’re not getting referenced.
This is where strategy matters. It’s also where most marketers are flying blind.
Generative engines don’t rank documents. They generate answers. And every time they do, they’re deciding who gets mentioned, which sources get cited, and what stories get told.
That decision is happening whether or not you’re watching.
Differences in Optimizing for Keyword Rankings (SEO) vs Visibility and Trust Inside LLMs (GEO)
It’s easy to assume that SEO and GEO are two sides of the same coin. Same audience. Same content. Just add “AI” to your title tag and call it a day, right?
Not quite.
The differences go deeper than format or channel. They reflect two entirely different systems for how information is discovered, interpreted, and prioritized.
With traditional SEO, your content is evaluated by a structured algorithm that crawls, indexes, and ranks pages based on known signals. You’re optimizing for position. The higher you rank, the more likely someone clicks and lands on your site.
With GEO, the model isn’t ranking pages. It’s generating answers. You’re optimizing for inclusion and credibility. It decides if your brand gets mentioned at all, and if so, how favorably and how early in the answer.
Here’s a breakdown of what that looks like in practice.
How Traditional SEO Prioritizes Keywords and Ranking Signals
For over two decades, SEO has been built around a few key buckets. Each one plays a role in helping your content surface when users enter a relevant search.
1. Content Strategy
This is where most SEO programs live or die. You’re building content that targets specific keywords your audience is searching for. The best strategies are mapped to user intent and journey stage, balancing education and conversion.
Common examples:
- Blog posts that rank for “how to reduce churn in SaaS”
- Pillar pages like “B2B SEO Services”
- Long-form guides targeting niche verticals
The goal is to rank highly for relevant queries and answer them well enough that the user stays, explores, and hopefully converts.
2. On-Page Signals
Google doesn’t just look at your words. It looks at how you structure them.
That means clear title tags, logical header hierarchy (H1, H2, H3), internal links that guide users deeper, and metadata that matches the user’s expectations.
Examples:
- Optimizing title tags to include the primary keyword near the beginning
- Using FAQ schema to get featured snippets
- Linking related blog posts to pass internal authority
These signals help the search engine understand what your page is about and how it relates to others.
3. Off-Site Signals (Backlinks)
Google still leans heavily on backlinks to judge authority. The more reputable sites that link to you, the more trustworthy you appear in the eyes of the algorithm.
Examples:
- Getting mentioned in a TechCrunch article
- Contributing a guest post to an industry blog
- Earning links from tools directories or comparison sites
Backlinks are still a foundational currency in SEO. Not all are equal, but a strong link profile can give you an edge over competitors who may have similar content.
4. Technical SEO
None of your work matters if the search engine can’t crawl or index your site. This is the plumbing behind the scenes.
You’re making sure pages load fast, your site works on mobile, your sitemap is accurate, and there’s no duplicate or broken content blocking your momentum.
Examples:
- Compressing images and removing bloated scripts for speed
- Ensuring canonical tags are correct to avoid duplicate content
- Keeping a clean and crawlable URL structure
This isn’t glamorous work, but it’s essential. Great content on a slow, messy site is like having a killer storefront with the lights off.
In traditional SEO, all of this works together to help you rank higher and earn that click. It’s built around known signals and consistent benchmarks.
Now, let’s look at what changes when the algorithm is replaced by a generative engine that builds the answer for you.
How GEO Prioritizes LLM Visibility and Brand Trust
If SEO is about being found, GEO is about being remembered.
Your goal isn’t to climb a results page. It’s to show up directly inside the answer, framed accurately and favorably, when someone asks a question the model thinks your brand should be part of.
Here’s how that works in practice.
1. Content Framing and Format
The content that works best in LLMs doesn’t just answer questions. It does so in ways that are easy for the model to understand, summarize, and reuse.
That means format matters as much as the content itself.
Examples:
- “Best-of” lists like “Top AI Tools for Customer Support”
- Versus pages that compare tools side by side
- FAQ blocks with clean, direct answers
- Original research or stats that get cited by third-party blogs
These formats give the model structured, digestible content it can pull from.
They also mirror the kinds of questions buyers are asking, which increases your chances of being included in LLM outputs.
2. Trust Signals and Citability
LLMs don’t just look at your site. They draw from the broader web to decide what sources are credible. That means your presence on third-party platforms matters more than ever.
Examples:
- Being mentioned in high-authority roundups
- Cited in industry blogs, Product Hunt threads, or research reports
- Consistent naming and messaging across all properties, including your LinkedIn and help center
Think of it this way. If the model sees your name mentioned by other trusted sources, that’s the modern equivalent of a backlink.
It makes you more likely to be included as a reference in the response.
3. Model Interpretability and Structure
Even the best content can be ignored if it isn’t structured in a way that models can parse and trust. That’s where technical clarity comes in.
Examples:
- Using schema markup like FAQPage or Product
- Including clear author bylines and sources
- Avoiding vague language or opinion-led copy without supporting details
The more factual, structured, and clearly cited your content is, the more likely an LLM will use it.
This is also how you reduce hallucinations and keep the AI from describing your company incorrectly.
4. LLM Visibility Monitoring and Prompt Coverage
Here’s the big shift. In SEO, you track keyword rankings. In GEO, you track prompts and presence.
You need to understand which prompts your buyers are actually typing into generative tools, whether your brand shows up, and how it’s framed.
Examples:
- Creating structured prompt sets across the buyer journey
- Running tests in ChatGPT, Claude, and Perplexity to see where you’re mentioned
- Using tools like Scrunch, Profound, or Otterly to monitor brand visibility in LLM outputs
You can’t optimize what you don’t measure. GEO requires a repeatable process to track and improve your brand’s inclusion in AI-generated answers over time.
In SEO, you’re optimizing for visibility inside a results page. In GEO, you’re optimizing for trust inside an answer. The mechanics are different, but the goal is the same.
Be the brand they find. Be the one they remember.
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