SEO vs. GEO: A Practical Guide to Traditional & AI Search Optimization

Author:
Kevin McLauchlin

By Joe Collins, Resident AI Geek @ Cadence SEO

SEO (Search Engine Optimization) focuses on ranking in traditional search engines like Google, while GEO (Generative Engine Optimization) focuses on getting your brand and content cited inside AI-generated answers from platforms like ChatGPT, Google AI Overviews, and Perplexity. Both matter, but they work differently.

Our in-depth guide breaks down the differences, where each strategy works best, and how to adapt your content to perform across both search and AI platforms.

Table of Contents

What Is SEO?
The Role of Keywords in SEO
On-Page and Off-Page SEO
The Importance of Traditional SEO
What Is GEO (Generative Engine Optimization)?
How AI Systems Build Recommendations
SEO vs. GEO: Quick Reference Table
How GEO Impacts SEO Content
How to Build a Strategy for SEO & GEO Visibility

Traditional SEO

The foundation, how it works, and why it still matters.

What Traditional SEO Is

Put simply, traditional SEO is the practice of optimizing web content so that it ranks well in search engine results pages, primarily on Google and Bing. The goal is visibility; when someone types a query into a search engine, you want your page to appear as high as possible in the results.

The underlying mechanism is a search index. Search engines crawl the web continuously, store information about every page they find, and then use algorithms to rank those pages against each other when a query is submitted. The ranking is based on hundreds of signals, but the most significant ones fall into three distinct categories:

Relevance

Does this page actually address what the user searched for? Relevance is evaluated at the level of keywords, topic coverage, content structure, and semantic meaning.

Authority

How much does the web trust this page and this domain? Authority is built primarily through backlinks from other credible sites, though brand signals and engagement data also factor in.

Experience

Does the page deliver a good user experience? Page speed, mobile usability, Core Web Vitals, and clear information architecture all matter here.

The Role of Keywords in Traditional SEO

Keywords are the “core unit” of traditional SEO. When someone types “best noise cancelling headphones under $200” into Google, the search engine evaluates which pages best match that specific query, matching content to intent. Historically, this was pretty literal: pages with that exact phrase on them ranked better.

This is an evolving space, though, and modern search algorithms, particularly Google’s, have become significantly more sophisticated. Google now understands semantic meaning, synonym relationships, and search intent, not just keyword strings. A page about “affordable wireless headphones with noise cancellation” can rank for the same query as one that uses the exact phrase as its title. But keywords still anchor the process. You need to know what your audience is searching for, use that language deliberately in your content, and structure your pages so Google can confirm relevance quickly.

The keyword research process in traditional SEO involves identifying which terms have meaningful search volume, assessing how competitive those terms are (keyword difficulty/KD), and mapping specific keywords to specific pages on the site. Each core page earns the right to target a primary keyword, supported by variants and related terms.

On-Page and Off-Page SEO Factors

Traditional SEO work divides broadly into what you control on the page itself and what happens off it.

On-Page SEO

On-page SEO refers to everything on the page we can directly optimize: title tags, meta descriptions, heading structure (H1 through H3 primarily), body content, internal links, image alt text, and URL structure. The goal is to make the page’s topic and relevance crystal clear to both the search engine and the reader. Well-structured content that directly addresses a query with enough depth and specificity consistently outperforms thin or loosely organized pages.

Off-Page SEO

Off-page SEO refers primarily to backlinks, or other websites linking to your content. A backlink from a credible, relevant source functions as a vote of confidence in the eyes of a search engine. The quantity and quality of backlinks pointing to a page or domain is one of the strongest ranking signals that exists in traditional SEO. Earning these links requires good content, outreach, digital PR, and sometimes partnerships.

Technical SEO

Beneath the content and links sits a technical layer that affects how well search engines can crawl, index, and understand a site. Technical SEO covers things like site architecture, page speed, mobile-friendliness, structured data markup, crawl budget, canonicalization (and a whole lot more). A site with excellent content but serious technical problems, like pages that can’t be indexed or that load in five seconds on mobile, will underperform relative to its content quality. Technical health is the foundation on which everything else sits, which is why we constantly review and evaluate it for our clients.

Why Traditional SEO Still Matters

With AI search growing rapidly, it’s fair to ask whether traditional SEO is becoming less important. It’s arguably one of the most popular questions you see asked on Linkedin these days among marketers. The short answer is that it is not, at least not yet, and probably not entirely ever. Here’s a look at why:

Search engines still drive a significant share of web traffic, and that’s unlikely to disappear any time soon. Most commercial and transactional queries still route through traditional search.
AI search systems, including those from Google and Microsoft, still rely on indexed web content as their source material. A page that cannot be crawled and indexed cannot be cited by an AI either.
The ranking signals that matter for traditional SEO–authority, relevance, clarity, depth, etc.–lso happen to be the signals that make content more likely to be cited by generative AI systems.
Local, transactional, and navigational search intent is still handled most efficiently by traditional search results, not AI-generated summaries.

We tend to think of traditional SEO as the prerequisite. You need to get the fundamentals right before GEO becomes relevant.

GEO and How AI Builds Answers

Generative Engine Optimization and the mechanics behind AI recommendations.

What GEO Is

In super simple terms, Generative Engine Optimization (GEO) is the practice of structuring content so that it’s more likely to be retrieved, cited, or directly quoted by AI-powered answer systems. These include LLMs like ChatGPT with web search enabled, Google’s AI Overviews, Microsoft Copilot, Perplexity, and similar tools that generate synthesized answers rather than presenting a list of links. And the list continues to grow every day.

Where traditional SEO focuses on ranking on page one of SERPs, GEO looks at getting cited in the answer. That distinction matters a lot, because the mechanism is completely different.

How AI Systems Build Recommendations

This is one of the most important concepts to understand, and probably why you’re reading our guide. Let’s look now at how a large language model (LLM) like the ones behind ChatGPT or Claude actually process a question and arrive at a recommendation. Understanding this is the core of GEO.

Training vs. Retrieval

There are two fundamentally different ways an AI system can answer a question. The first is from training data; an LLM is trained on an enormous mass of text drawn from books, websites, academic papers, and a litany of other sources. During training, the model develops a compressed, weighted representation of that knowledge. When you ask it a question without giving it access to the web, it’ll draw on that learned representation to generate a response.

The second is retrieval-augmented generation, or RAG. This is what happens when an AI system like ChatGPT with web search enabled (or Perplexity, Claude, etc.) runs live queries against a search index, retrieves pages, reads them, and then uses that retrieved content to inform its response. GEO is mostly concerned with this second mode, because that’s where your content has a chance to be cited.

How a Training-Based Response Is Built

When an LLM generates a response from training data alone, it’s not looking up an answer in a database. It is predicting the most useful and coherent response based on patterns learned across its entire training database. Content that appeared frequently, consistently, and in well-structured form across many sources during training is much more likely to be reflected in those predictions.

This has real and serious implications. A brand, product, or perspective that’s well-represented in credible, widely-linked, clearly-written sources across the web will be better represented in an LLM’s training than one that exists only in a single well-optimized page (no matter how well you do the optimization). This is a major reason why digital PR, thought leadership, and earning coverage from authoritative third-party sources matters for GEO even when no live search retrieval is involved.

When an LLM synthesizes a recommendation, it’s also doing something more than retrieving facts. It is evaluating what is consistent across sources, what appears to be consensus versus outlier opinion, which claims are well-supported (versus asserted without backing), and how to present the information in a way that matches the understood intent of the question being asked. Clarity, structure, and specificity in your content all improve the probability that the information you want to communicate is the information that gets surfaced.

Query Fan-Out: Probably the Most Important Concept in AI Search

When a user submits a prompt to an LLM that has web search capability, the AI doesn’t simply run that exact query against a search index. That would just mean LLMs automate typing a search into Google. Instead, it breaks the prompt down into multiple sub-queries, a process called query fan-out.

Let’s say a user is asking “What’s the best approach to podcast advertising for a direct-to-consumer brand?”. That might generate sub-queries like “podcast advertising effectiveness for DTC brands”, “host-read vs. produced podcast ads performance”, “podcast ad attribution methods”, and “podcast advertising CPM benchmarks”. Each of those sub-queries goes to a search index independently, and the results are retrieved and combined before the AI drafts its final answer.

This behavior fundamentally changes what it means to optimize for AI search, and it’s where traditional SEO and GEO part ways.

Reciprocal Rank Fusion (RRF)

Once the AI generates its fan-out sub-queries and retrieves results for each, it needs a way to evaluate which sources are most worth incorporating into the final response. One method used for this is called Reciprocal Rank Fusion, or RRF.

RRF scores pages based on how well they perform across the full set of sub-queries, not just one. A page that ranks strongly for 3 out of 5 sub-queries will score higher than a page that ranks first for only one. This has a direct consequence for content strategy: pages with deep topical coverage that naturally address multiple related questions simultaneously are mathematically more likely to be cited than pages optimized narrowly for a single keyword.

A buying guide that covers multiple product types, compares options, addresses common questions, and discusses pricing will consistently outperform a page optimized for a single head term. That buying guide answers more of the fan-out queries and earns a higher combined score.

Passage-Level Retrieval

AI systems don’t necessarily evaluate a page as a single unit, either. They’re capable of retrieving and citing specific passages within a page, meaning a sentence or paragraph that directly and cleanly answers a sub-query can be surfaced even if the rest of the page is only loosely relevant.

This is why sentence-level clarity matters in page content. If your content contains a passage that says (preferably in plain and direct terms) exactly what an AI sub-query is looking for, that passage is a candidate for direct citation or quotation. Vague, poorly-worded or overly complex copy is less likely to be pulled at the passage level than clear and concise statements that directly answer a specific question.

Structuring content with natural-language follow-up questions as H2 or H3 headings helps capture this behavior. A heading like “How does audience targeting work in programmatic podcast advertising” signals to the AI exactly what the passage below it addresses, making it easier for the retrieval system to match that passage to a relevant sub-query.

SEO vs. GEO: Side-by-Side Comparison

How traditional SEO and GEO differ across the dimensions that matter most.

With this foundation in place, the differences between SEO and GEO become much clearer.

	Traditional SEO	GEO (Generative Engine Optimization)
Success metric	Ranking position on a search results page.	Being cited or quoted in a generated AI response.
Primary optimization target	A specific keyword or keyword set mapped to a page.	Topical coverage that satisfies multiple related sub-queries simultaneously.
Content strategy	One primary keyword per page, supported by variants. Avoid cannibalizing other pages.	Deep, comprehensive pages that answer many related questions. Breadth and specificity coexist.
Keyword matching	Exact and semantic match to the user’s query is central to ranking.	Sub-queries are generated by the AI and may not match the user’s original phrasing at all.
Authority signals	Backlinks from credible domains drive domain and page authority.	Brand presence across credible third-party sources signals trustworthiness to training data and retrieval systems.
Content structure	Title tags, meta descriptions, heading hierarchy, and internal links are primary structural signals.	Natural-language headings framed as questions, concise and direct passage-level writing, and clear entity attribution matter most.
Traffic delivery	Ranking pages drive clicks to the site.	AI may answer the question completely without a click. Visibility is the metric, not necessarily traffic.
Measurement	Rank tracking, organic sessions, impressions, and CTR in GSC.	Citation monitoring, brand mention tracking in AI responses, and prompt-based audits.
Technical foundation	Crawlability, indexation, and page speed are prerequisites.	The same prerequisites apply. Content that cannot be indexed cannot be retrieved by AI search either.
Content update cadence	Refresh content to maintain ranking position and reflect new information.	Freshness matters because AI retrieval systems weight recently updated, accurate content more heavily.
Long-tail strategy	Long-tail keywords target specific, lower-competition queries on individual pages.	Long-tail coverage matters because fan-out queries are almost always long-tail. In other words, we’re looking to cover the same queries, presented differently.

TL;DR: SEO or GEO?

So what does all this really mean? Here’s the TL;DR:

Traditional SEO and GEO are not competing disciplines. They’re sequential layers of the same goal: making sure content is found, understood, and trusted by whatever system a person uses to find an answer.
Get the technical foundation right. Build topical authority through deep, well-structured content, earn credibility from external sources, and write clearly enough that a single sentence from your page can stand alone as a useful answer.
All of this really is good SEO at its core, and it’s also good GEO. The difference is in understanding which systems are reading your content and why they make the choices they do.

Here’s how GEO shapes content strategies.

The Fundamentals Haven’t Changed, But the Stakes Are Higher

The single most important takeaway from this guide is that good content has always been the goal, and GEO doesn’t change that. What GEO does, though, is raise the stakes for depth, structure, and specificity. Content that is thin, vague, or poorly organized has always underperformed in traditional SEO. In AI search, it simply has no chance at all.

A page with a clear topic, direct answers to specific questions, well-structured headings, and genuine depth performs well across both traditional search and AI retrieval. There’s no version of good GEO that isn’t also good SEO.

Building a Strategy for Your SEO & GEO Visibility

Search visibility now extends beyond rankings into AI-generated answers, where brands are recommended, summarized, and cited without users ever visiting a traditional results page. If you want to understand where you stand and where you can improve, our CadenceSEO team can help.

Request a free live audit.

During this session, we will:

Evaluate technical factors that may be limiting performance
Analyze your position relative to competitors across search and AI visibility
Identify content and on-page gaps that impact how your site is interpreted
Outline a practical strategy for improving performance
Answer any questions about your current approach and next steps

The session takes approximately 15–30 minutes and is designed to provide immediate clarity, with no obligation.

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References:

Kevin McLauchlin

Kevin is one of the Co-Founders of CadenceSEO. He has spent the last 5 years living and breathing SEO as well as other Digital Marketing channels. Outside of work he is an Ultra-Runner and father of 6 amazing kids.