---
title: GEO Requires More Than SEO
date: 2026-04-20T10:00:00-05:00
author: Dario Zadro
canonical_url: "https://zadroweb.com/blog/seo-vs-geo/"
section: Articles
---
GEO is mostly SEO.

But mostly is not all of it. That gap, small as it currently is, matters more than the industry wants to admit.

The conversation keeps collapsing into two useless camps.

**Camp one** says GEO is just repackaged SEO (with a shinier invoice).

**Camp two** says SEO is dead and LLMs are taking over (the world).

I've been managing SEO and GEO for clients long enough to have actual data on this.

LLM-referred traffic currently sits around 2.5% for most sites I manage. Low? Sure. But those same visitors are converting at 20% or higher. That's a different kind of user showing up with intent already baked in. So before anyone waves off GEO as a buzzword, the conversion signal alone deserves serious attention.

![AI search traffic statistics](https://static.zadroweb.com/site/_larger/ai-traffic-case-study.png?transformId=1341)Ahrefs Web AnalyticsAI search sits at 2.4% of total traffic here, but the engagement metrics tell a different story. Lower bounce rate than paid search. Visit duration matching direct traffic. These are not casual visitors.

So, if we're separating Search and AI visitors in our metrics, there has to be gap in how we differentiate the two.

Let's get into it.

## Eligibility vs. Selection: The Distinction Nobody Is Making

This is the root of nearly every bad GEO take in the market right now. Two completely separate problems are being treated as one.

**Eligibility** is whether an AI system can reach your content at all. Crawled, indexed, snippet-eligible. [Google explicitly documents](https://developers.google.com/search/docs/appearance/ai-features) that to be considered for AI Overviews, a page must be indexed and eligible to show a snippet. If you don't clear that bar, nothing else matters. The technicals actually go much deeper, but that's for another article.

**Selection** is what happens after retrieval fires. Of everything the system pulled in, which *chunks* actually end up in the LLM response?

SEO governs eligibility. GEO governs selection. These are sequential problems, not the same problem. And treating eligibility as sufficient for selection is exactly where most practitioners get stuck.

The data backs this up. A [March 2026 Ahrefs analysis](https://ahrefs.com/blog/ai-overview-citations-top-10/) of 863,000 keywords and 4 million AI Overview URLs found that only **38% of cited pages ranked in the top 10** for the same query, down from 76% just seven months earlier.

A separate [BrightEdge analysis from February 2026](https://www.brightedge.com/resources/weekly-ai-search-insights/ai-overviews-one-year-presence-size-citing) puts that overlap even lower, at around 17%, depending on methodology. The remaining Ahrefs citations split nearly evenly between positions 11-100 (31.2%) and pages that don't appear in the top 100 at all (31%).

So roughly two out of three AI citations come from pages a user would never see on page one. The AI is not drawing only from what ranks. It's drawing from what it can *use*.

> SEO governs eligibility. GEO governs selection. Treating them as the same problem is where strategies break down.

Dario Zadro Zadro Web## RAG: A Term the SEO Community Is Misusing

You can't open a LinkedIn feed right now without someone referencing RAG. Most of those posts are technically wrong. And the misuse matters because it leads to the wrong strategy.

[Retrieval-Augmented Generation](https://arxiv.org/abs/2005.11401) is a specific architecture. It combines what a model already knows from training (parametric knowledge) with documents pulled in at query time (non-parametric retrieved context). That's it.

RAG is **not**:

- A synonym for "search API call" as many SEOs seem to parrot.
- The same thing as embeddings, which are a vector representation method used *within* retrieval, not the architecture itself.
- Something that always happens. Many queries get model-only responses with no retrieval at all.

That last point gets overlooked almost universally. Retrieval is conditional, not constant. When someone tells you "rank well and you'll get retrieved," they're giving you advice that only applies to a subset of queries and skipping the harder question of what happens after retrieval fires.

### Not All Retrieval Runs Through Google or Bing

A popular claim in SEO circles right now is that every URL appearing in an LLM output comes from a search engine API. Google or Bing, full stop. That's a useful heuristic for the Google AI Overviews case specifically. But applying it as a universal law across all LLMs is where the argument breaks down fast.

Here's what the documented retrieval landscape actually looks like:

- **Google AI Mode / AI Overviews.** Google [explicitly describes](https://developers.google.com/search/docs/appearance/ai-features) query fan-out across subtopics and data sources, with supporting pages identified *during* response generation. The citation set can extend well beyond what ranked for the original query.
- **Claude.** Runs on Brave Search, not Google or Bing. A 2025 analysis by Profound found [86.7% overlap](https://www.tryprofound.com/blog/what-is-claude-web-search-explained) between Claude citations and Brave's top organic results, a finding independently confirmed by TechCrunch when Anthropic added Brave to its subprocessor documentation. Not indexed by Brave? Claude won't cite you. Doesn't matter where you rank on Google.
- **Perplexity.** Runs its [own continuously refreshed index](https://docs.perplexity.ai/docs/resources/perplexity-crawlers) using two distinct crawlers, after getting away from using Bing Web Search API back in 2022. Strong freshness bias, where 90% of top cited sources answer the core question within the first 100 words (BLUF pattern).
- **Microsoft Copilot.** [Grounds through Bing APIs](https://learn.microsoft.com/en-us/microsoft-copilot-studio/data-privacy-security-web-search) across three distinct modes: specific URLs, open web search, or custom search.
- **OpenAI Assistants.** [Documents hybrid keyword plus semantic vector retrieval](https://platform.openai.com/docs/assistants/tools/file-search), query rewriting, parallel searches, and reranking. No search API involved.

Not all retrieval is the same. And underneath all of it, two distinct mechanisms are actually happening that most SEOs and agencies never separate.

The first is training data inclusion. Being present in the sources that models learned from. Wikipedia, major publications, Reddit, high-authority industry sites. You build toward it through long-term brand authority and earning mentions across those sources. Sound familiar? It should. That's digital PR and link outreach. SEO under the hood.

And Google has been parsing contextual relevance and brand mentions longer than most realize. But the real divergence is sentiment parsing across trained data and RAG methods. **Feeding those systems is where SEO ends and GEO begins**.

The second is retrieval-time inclusion. It's what happens when an LLM does live web retrieval during a prompt response. Results get re-ranked by the model based on probability and relevance. And that retrieval step does not care much about rankings.

That second mechanism is what 90% of GEO tactics actually target. Even when SEOs claim otherwise.

Your brand appearing as a citation is an output address. It's not a retrieval receipt. It doesn't tell you how the content got there, and here is where the GEO nuance needs to be accounted for.

## What Actually Gets You Selected

The foundational GEO research from Princeton ([arXiv 2311.09735v3](https://arxiv.org/html/2311.09735v3)) tested specific content modifications across generative engines and measured citation visibility changes:

TacticVisibility LiftStatistics addition+40%Quotation addition+40%Inline citations+30-40%Fluency optimization+15-30%Authoritative tone+10-15%Stats, quotes, and citations. Every time.

But here's the nuance most people skip. There's no single universal "write like this and get cited" formula. What lifts citation rates in one vertical can actively hurt another. Word count matters in SaaS. It works against you in Finance. Structure that wins in one niche falls flat in another.

And the one writing signal that holds universally: declarative language in your intro. "X is Y." Not hedging, not context-setting. Use direct statements. Hedging language in your opening paragraph actively suppresses citation rates. That's the first thing to fix before anything else.

## The Five Selection Signals That Determine What Gets Pulled

After retrieval fires, something decides which specific chunks make it into the response. Think of it less like a ranking algorithm and more like an editor scanning a document for quotable paragraphs.

These are **selection signals**, not ranking factors. The framing matters. You're not trying to rank a page. You're trying to get a paragraph pulled.

**1. Extractability.** Can the system isolate a standalone, self-contained chunk? A paragraph that requires three surrounding paragraphs to make sense won't survive chunking. [OpenAI's documentation](https://platform.openai.com/docs/assistants/tools/file-search) on `file_search` treats chunking as a first-class retrieval step, with a default chunk size of 800 tokens and 400-token overlap. Structure isn't cosmetic. It's functional.

**2. Verifiability.** Stats, attributed quotes, inline citations. I keep coming back to this because the data keeps confirming it. Adding a single relevant statistic with a named source can lift AI citation visibility by 40%. That's not a minor content tweak. That's structural. And in my experience tracking citation patterns across client sites, content anchored to specific numbers and dates consistently outperforms vague qualitative claims. "Conversion rates increased 23% over six months" gets pulled. "Conversion rates improved significantly" gets ignored.

**3. Entity clarity.** Define the brand, product, or person you're writing about early. Keep naming consistent throughout. [iPullRank's analysis of AI Mode mechanics](https://ipullrank.com/how-ai-mode-works) points to passage-level entity grounding as a key part of how content gets matched and surfaced. Ambiguity is a filter-out signal, not a neutral one.

**4. Chunk-level independence.** Every meaningful section should answer a question on its own. Here's a test I use: copy a paragraph out of context and paste it directly into an AI query. Does it read as a complete, citable claim? If not, it probably won't get selected. If yes, you're in the game.

**5. Fan-out coverage.** This one connects directly to [topical authority and silo architecture](https://zadroweb.com/blog/topical-authority-silo-architecture/), a framework I've been writing about long before it had a GEO angle attached to it. A [2025 Surfer SEO study](https://surferseo.com/blog/query-fan-out-impact/) analyzing 173,902 URLs found that pages ranking for fan-out queries are **161% more likely to be cited** in AI Overviews than pages ranking only for the main query. The Spearman correlation between fan-out coverage and citation likelihood was 0.77, which is a strong signal. And ranking for fan-out sub-queries alone was **49% more likely to earn a citation** than ranking exclusively for the head term.

Building a tight content cluster around a topic isn't just traditional SEO strategy anymore. It's GEO necessity.

## What SEO Still Does (And Doesn't Do)

None of this means SEO doesn't matter. But the reasons it matters are more specific than the community tends to acknowledge.

**Indexing equals eligibility.** Google requires pages to be indexed and snippet-eligible to be considered for AI features. Not crawled and indexed? Not in the pool. No amount of selection signal work saves you from that.

**Authority signals affect trust weighting.** Domain authority, quality backlinks, E-E-A-T. These function as quality indicators. I've watched lower-authority sites with specific, citable content outperform higher-authority sites writing in vague generalities. Authority helps. It doesn't compensate for weak content structure.

**Rankings are a probability multiplier, not a guarantee.** Higher rankings increase citation odds. They don't lock it in. And they don't lock out lower-ranked content. Around 31% of AI citations come from pages outside the top 100. Rank is an input, not a guarantee for LLM inclusion.

I've covered the technical side of Google's AI retrieval in the [Google AI Mode Playbook](https://zadroweb.com/blog/googles-ai-mode-complete-playbook-for-seo-survival/). Worth a read if you want to go deeper on how eligibility and indexing intersect with AI feature inclusion.

## Measuring GEO

Most GEO measurement advice stops at "check if you're showing up in AI." That's a start. But citation presence alone doesn't tell you why you're being selected, which content chunks are getting pulled, or where your topical coverage has gaps. Here's what actually moves the needle.

**Citation presence.** Are you showing up in AI Overviews, ChatGPT, and Perplexity for your target queries? Manual spot-checking works at small scale. Tools like Gumshoe, Profound, Semrush, and Ahrefs are helpful, as they provide valuable directional data. Best effort, but genuinely useful signals.

**Fan-out visibility.** Open dev tools on any AI chat interface, fire a query, and inspect the Network tab. What you'll see are sequential `tool_use` blocks in JSON (Claude). This shows your single query decomposed into multiple sub-queries. That's fan-out made visible. And it tells you exactly which sub-query variations your content needs to cover to survive.

The measurements and signals are available for those putting in the work. And again, this is SEO-leaning, but nuanced enough to call GEO a separate channel.

## The Real Model

Here's how most of the SEO community is currently thinking about this:

*Rank. Get retrieved. Get cited.*

Here's what's actually happening:

*Be indexable. Be verifiable. Be extractable at the chunk level. Have fan-out coverage. Increase selection probability.*

Those are not the same instructions. The first tells you to do SEO. The second tells you to do SEO, and then do something meaningfully different afterward.

Here's a realistic split: 70% of effective GEO is SEO. 20% is new tactical work around extractability and entity signals. And 10% is genuinely novel. AI visibility monitoring, citation tracking, scrubbing fan-outs, and earning brand mentions inside answers users actually trust.

GEO is real. SEO still feeds it. The gap between ranking and being cited is only going to widen as people continue to live more and more in their preferred LLM interface.

GEOs who close that gap now won't need to scramble when it becomes obvious to everyone else.

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 ![Dario Zadro, Author at Zadro Web](https://www.gravatar.com/avatar/7fd3c6f03536f4ad85610768a8da304d?s=130&d=mm) 

### Dario Zadro

Dario Zadro is a full-stack developer and technical SEO with 20+ years of experience. Founder of Zadro Web, a web development and SEO agency operating since 2007, specializing in custom web development, SEO/GEO, and cloud infrastructure. He builds lean, maintainable systems, helping clients reduce technical debt.

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