AEO vs GEO: The Differences Explained (and What to Do First)

"Should I concentrate more on AEO/GEO or SEO?"

That question shows up constantly on r/SEO. And it's the wrong question. Not because the asker is confused, but because the industry made the acronyms confusing in the first place.

Here's the short answer: AEO and GEO are not the same thing. They optimize different surfaces, require different work, and get measured differently. If your "AI visibility" tool says your score is 0 and nothing happens next, you don't have a tool problem. You have a surface problem and a workflow problem.

Understanding AEO vs GEO is table stakes. The hard part is building the footprint that gets you cited and recommended everywhere AI looks for answers.

This article gives you a clear boundary between the two, a way to measure each when referrers disappear, and a decision framework for which one to start with.

Check if your brand appears in ChatGPT, Perplexity, and Google AI Overviews →

Are AEO and GEO the same thing?

No. They overlap, but they optimize different surfaces and success metrics.

Answer Engine Optimization (AEO) focuses on making your content extractable for answer features like Google AI Overviews and AI Mode. The primary job is eligibility: structuring your pages so AI systems can pull direct answers from them. Semrush defines AEO as optimizing content "to appear in AI-generated answers."

Generative Engine Optimization (GEO) is broader. It's about building the third-party footprint that makes AI models cite and recommend you. The term comes from Princeton researchers who studied how content optimization affects visibility in generative engines like ChatGPT and Perplexity.

Google's documentation on AI features focuses on eligibility criteria for their answer experiences. That's the AEO surface. GEO extends beyond Google into ChatGPT, Claude, Perplexity, and any model synthesizing answers with citations.

If you don't name the surface, you can't pick the right work.

What problem are AEO and GEO solving (and why it got urgent)?

AI answers are compressing clicks. But they're also increasing the value of being cited.

Pew Research found that in March 2025, 18% of Google searches produced an AI summary. When that summary appeared, users clicked on traditional results only 8% of the time. Without the AI summary, the click rate was 15%.

The scale is substantial. Google reports that AI Overviews reach more than 1 billion global users every month. Semrush's analysis of 10M+ keywords showed AI Overviews triggered for 6.49% of queries in January 2025, peaked at 24.61% in July 2025, and sat at 15.69% by November 2025.

This volatility matters. You can't set and forget.

Here's the opportunity: Semrush's AI traffic study found that the average AI search visitor converts 4.4x higher than traditional organic search visits. The study was scoped to marketing and SEO topics, so treat that as directional, not universal.

"(GenAI) solutions are becoming substitute answer engines, replacing user queries that previously may have been executed in traditional search engines." — Alan Antin, Vice President Analyst, Gartner

Success isn't just rankings anymore. It's being the source the model pulls from.

How to evaluate AEO vs GEO (6 questions)

Before diving into tactics, you need a framework for deciding which surface to prioritize. Ask these six questions:

1. What surface are you targeting? Are you trying to win Google AI Overviews and AI Mode-like behavior? Or LLM answer engines like ChatGPT and Perplexity?

2. What output do you need? Do you need "extracted answers" (your content lifted into a response) or "cited sources" (your URL listed as a reference) or "recommendations" (the model suggesting your brand as the best option)?

3. How will you measure it? Can you track this with Search Console and feature monitoring? Or do you need a prompt set and weekly diffs?

4. What does the work depend on? Does success depend on your own pages being clean and extractable? Or on third-party corroboration existing across the web?

5. Which surface can you influence fastest? Given your current assets, where can you make visible progress in 30 days?

6. Where is the brand risk? In high-trust categories, which mistakes create more damage: sloppy on-page answers or unsupported claims in third-party content?

seoClarity research found that 97% of AI Overviews cite at least one source from the top 20 organic results. That means the AEO surface still depends heavily on ranking. But GEO surfaces like ChatGPT draw from a broader web.

These criteria prevent you from "doing everything" and measuring nothing.

AEO vs GEO: what changes on the page, and what changes off the page?

This is where the difference gets concrete.

AEO is about extractability and eligibility. The work happens on your pages. You're making it easy for AI systems to lift answers from your content.

GEO is about corroboration and citation gravity. The work happens off your pages. You're building a third-party footprint (lists, reviews, comparisons, community presence) that makes models more likely to cite you.

Dimension	AEO (Answer Engine Optimization)	GEO (Generative Engine Optimization)
Primary surface	Search answer features (AI Overviews, AI Mode)	LLM answer engines (ChatGPT, Perplexity, Claude)
Primary job	Make your pages extractable and eligible	Build a third-party footprint that gets cited
Core artifacts	Answer blocks, clean IA, schema where relevant	Comparisons, citations, corroboration, community presence
Primary metric	Extracted/featured + cited in answer features	Cited/recommended across prompt sets
Fastest win	Fix the page-level "answer"	Fix the "why aren't we cited" gap

The Princeton GEO paper tested specific interventions. Adding citations, quotations, and statistics improved their visibility metric by 30-40%. Keyword stuffing offered little to no improvement.

If your only move is schema tweaks, you're doing 10% of the job.

The operational reality: Understanding AEO vs GEO is table stakes. The execution—tracking visibility across engines, engineering presence in communities and comparisons, leveraging expert time efficiently—is where most teams get stuck. That's the Track → Engineer → Leverage → Own system we build for clients.

How do you measure AEO vs GEO (when referrers disappear)?

Here's where practitioners get frustrated. As one r/TechSEO commenter put it: "LLMs don't pass referrers, everything just gets dumped into direct."

They're right. And that means you need to treat measurement as sampling.

"ChatGPT does tag itself and is making efforts to add more tracking. Occasionally, you'll see it in GA. But most of the time, LLM referrals show up as homepage traffic." — Chris Long, VP of Marketing, Go Fish Digital

Here's what to track instead:

Mention: Your brand is named in the output.

Citation: Your URL or domain is listed as a source.

Recommendation: The model suggests your brand or product as a best option.

These are different outcomes. A mention without a citation doesn't drive traffic. A citation without a recommendation might not drive leads. You need to know which one you're getting.

The practical workflow:

Build a baseline prompt set (20-50 prompts covering your key topics and competitors).
Run those prompts weekly across ChatGPT, Perplexity, and Google (with and without AI Overviews).
Capture outputs: screenshots, cited URLs, brand mentions, recommendation language.
Track changes over time. Did you gain citations? Did competitor mentions increase?

For Google AI Overviews specifically, you can use Search Console data on queries where AI features appear. But for ChatGPT and Perplexity, the prompt set is your only reliable signal.

If you can't distinguish mentions from citations from recommendations, your "visibility score" won't tell you what to do next.

If you can only do one thing first, should you start with AEO or GEO?

Start with the surface that matches your demand.

Choose AEO first if:

Your pages already rank for target queries
You're trying to get extracted into AI Overviews and AI Mode
Your pages are hard to lift answers from (long blocks, poor structure, no clear takeaways)

Choose GEO first if:

Competitors are cited and recommended across prompts and you're not
You're missing from third-party lists, comparisons, and community references
Your pages rank, but you don't show up in ChatGPT or Perplexity answers

Do both in parallel if:

You're in a high-trust category (healthcare, finance, legal) where you need both on-page eligibility and off-page corroboration
Your competitors are investing in both surfaces

seoClarity data shows AI Overviews appear for 30% of U.S. desktop keywords as of September 2025. That's substantial. But if your pages already rank and you're still invisible in AI answers, the gap is probably off-site.

The right first move depends on where you're invisible.

A simple weekly workflow (that makes the work compounding)

AEO and GEO wins come from cadence, not one-time optimization. Here's a workflow that compounds:

Week 1: Set baselines

Pick a prompt set (20-50 prompts covering your key topics).
Run prompts across ChatGPT, Perplexity, and Google AI Overviews.
Capture baseline: who gets cited, who gets mentioned, who gets recommended.
Document competitor positions.

Week 2+: Ship and sample

Pick one surface to work on this week: AEO (on-page fixes) or GEO (off-site placements).
Ship one citable asset: a comparison, checklist, FAQ, or proof-backed answer block.
Re-run a subset of prompts and compare to baseline.

Monthly: Review and adjust

Which prompts show improvement?
Which competitors gained or lost position?
What content types are getting cited?

The workflow is the moat. A one-time optimization isn't.

Ready to see where you're invisible?

We'll run your key queries through ChatGPT, Perplexity, and Google AI Overviews and show you exactly where competitors get cited and you don't. Takes 30 minutes.

Get your AI visibility audit →

Common misconceptions (and what gets people stuck)

Misconception 1: "AEO/GEO replaces SEO"

It doesn't. AEO and GEO are extensions of search work, not replacements. The ranking work still matters—seoClarity found that 97% of AI Overviews cite sources from the top 20 results. But ranking alone doesn't guarantee you'll be cited in AI answers.

Misconception 2: "Schema is the main lever"

Schema helps with eligibility and clarity. But "being cited" also depends on proof density (statistics, quotes, named sources) and third-party corroboration. If you have clean schema but no one else on the web validates your claims, models have less reason to cite you.

Misconception 3: "Visibility is one score"

Different AI surfaces behave differently. Google AI Overviews draw heavily from ranked results. ChatGPT synthesizes from a broader corpus with different citation patterns. Treating them as one score hides the work you need to do.

As one r/growthmarketing poster put it: "GEO doesn't fit neatly into existing buckets. It's not just SEO, and it's not just PR either."

You don't need more acronyms. You need clearer boundaries.

Frequently asked questions

Should I concentrate more on AEO/GEO or SEO?

You need all three. SEO gets you ranked. AEO makes your ranked pages extractable for answer features. GEO builds the off-site footprint that gets you cited in ChatGPT and Perplexity.

If you're just starting, SEO fundamentals still matter—without rankings, AEO has no surface to work with. But if you already rank and you're invisible in AI answers, the gap is probably AEO (on-page) or GEO (off-site).

How do you know when ChatGPT is mentioning your brand? And for what queries?

You can't see ChatGPT queries the way you see Google Search Console data. The practical solution is sampling: build a prompt set of 20-50 relevant queries, run them weekly, and track which ones mention your brand, cite your URLs, or recommend your products.

This question comes up constantly on r/bigseo. The answer is always the same: you sample and track, because there's no referrer data coming back.

What's the best AEO/GEO tracker?

Most tools focus on monitoring AI visibility scores, which is useful for trends but doesn't tell you what to do next. The more useful setup is a prompt library you own, plus a process for running those prompts weekly and diffing outputs.

For a comparison of tools, see our GEO tools compared guide.

Does Schema markup actually help LLMs cite you?

Schema helps with clarity and eligibility, especially for Google AI features. But there's no strong evidence that schema alone drives ChatGPT or Perplexity citations. The Princeton GEO research found that adding citations, statistics, and quotations had a bigger impact (30-40% improvement) than structural changes alone.

Use schema where it's relevant (FAQs, products, organizations). But don't expect it to be the main lever.

Why does ChatGPT traffic show up as direct in analytics?

LLMs don't pass referrer data consistently. As one r/TechSEO commenter put it: "LLMs don't pass referrers, everything just gets dumped into direct."

Some platforms are working on attribution (ChatGPT sometimes tags itself). But for now, the reliable signal is prompt-level visibility, not traffic source reports.

What to do next

AEO and GEO are two surfaces, not one. AEO makes your content extractable for answer features. GEO builds the third-party footprint that gets you cited and recommended.

The key takeaways:

Know your surface: Google AI Overviews draw from ranked results. ChatGPT and Perplexity draw from a broader corpus. Different surfaces, different work.
Measure what matters: Separate mentions from citations from recommendations. A visibility score without these distinctions is noise.
Build a cadence: Weekly prompts, weekly outputs, weekly diffs. The workflow is the moat.

Understanding the difference is the foundation. The operational work—tracking your AI visibility, engineering presence across channels, building systems you own—is where most teams get stuck.

Not ready for an audit? Read the definitive guide to GEO →

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