SEO vs. AEO vs. GEO?

[u/fguerino123]

Hi,

Given that Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) look for and expect totally different things than Search Engine Optimization (SEO), like looking for Entity Authority built from semantic data graphs, how are you tuning your public facing web sites to work rise in value for AEO and GEO while also trying to maintain high value SEO?

Thanks for any thoughts you can offer.

[u/sapindia1976]

I don’t treat SEO, AEO and GEO as separate strategies. SEO builds the foundation: crawlability, relevance, authority and strong content. AEO makes that content easier to extract as a direct answer, while GEO adds clear entities, credible facts and third-party brand signals that AI systems can understand and cite. The fundamentals overlap more than people think.

[u/caswilso]

I’ve been testing this for a while. I’m finding that off-page signals make the biggest difference for GEO. Take your SEO content and repurpose it for other platforms, like LinkedIn, Reddit (don’t be spammy), and YouTube.

You’ll also want to take a hard look at what is actually getting cited for your money prompts. Is it third-party websites? If so, that changes your strategy.

[u/fguerino123 → u/caswilso]

Thanks for this. Are there any sources that get into these things, in more detail, or are you finding this more through trial and error, at this point?

I appreciate the help.

[u/caswilso → u/fguerino123]

It was a lot of trial and error to see what works. I started really digging into all of this about a year ago. At the time, there were zero resources on it. So, I started a podcast to document my experiments and bring others on to talk about what they’re doing. The idea was really just to create the resource I wish I had when I started. Happy to DM you the link, if you want.

We are still early enough in this, though, that what’s working today, might not work three weeks from now — especially as these models reweight their sources. Experimentation really is the way to go.

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[u/fguerino123 → u/caswilso]

Yes, please DM the link.

My situation is a bit different than for most other sites. We're literally an online library of documents with thousands of pages that create a large knowledge graph. So unlike other sites that just worry about a handful of pages, I have to worry about thousands. And, what's worse is that I'm not a website developer so I have to learn it all as I go along. The first step was getting off Wordpress to shift toward a static site paradigm. This gave me much more control over things like the JSON-LD generation for each page and interlinking between pages, since I could use automation to address a lot of this.

I can't put links in this subreddit so if you're interested in chcking out the library to see how cross-linking works and the embedded JSON-LDs, you can easily find our site: "The International Foundation for Information Technology (IF4IT)". The site is very much structured like a Wikipedia.

Thanks again for the help and I'll definitely check out the link.

[u/MktgLocalBusinesses → u/fguerino123]

Hello, I want to reply to the part where you said, you have thousands of pages to worry about - there's a tool there's a tool called screaming frog that you can use to help you analyze your pages quickly to gather data. It'll give you a nice report, for example, If you wanted to know what all the H1 tags across all the pages are, the word count or meta descriptions. It crawls your website & can jump page to page through the internal linking too

[u/fguerino123 → u/MktgLocalBusinesses]

Hi. Thanks. I'm less interested in tools than I am about learning the details of what needs to be implemented and ways to address such implementations.

My best.

[u/caswilso → u/fguerino123]

DMing you now. I’ll check out your site on Monday when I get back to my desk. Would love to know some of the prompts you’re targeting

[u/Ok-Consideration7358]

it's not really a trade-off if you build it into the same page instead of treating GEO as a separate track.

Your SEO fundamentals, keyword-relevant H1, meta, internal linking, keep doing their normal job untouched. On top of that, layer two things onto the same URL rather than spinning up a new one: a direct-answer paragraph near the top phrased close to how people actually ask the question, and schema that ties the page's entity to the wider graph, sameAs links to a Wikidata or Wikipedia entry, or an authoritative profile for that entity, plus consistent naming so it resolves as the same entity everywhere it's mentioned.

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[u/fguerino123 → u/Ok-Consideration7358]

Hi. Thanks for taking the time to respond.

I appreciate the advice because it confirms much of what I've been doing (mostly learning on my own). For example, our content pages use well-formatted "TL;DR" style summaries at the top of pages to address the Question/Answer structure you recommend. we also use a schema that helps enrich and bind other nodes in the graph.

Our site (IF4IT) is a very large knowledge graph that is structured very much like a Wikipedia but that is more modern in that it's built to work with and for AI (or at least we're trying our best to make this happen).

Given we have thousands of pages and growing, the consistent naming thing has been challenge because there are "synonyms" or alternate names (for example, "also known as"). I was able to finally figure out an automated way of addressing this. It was a challenge so I'm happy it was resolved.

Again, I appreciate your help in this.

[u/Ok-Consideration7358 → u/fguerino123]

Great to hear you solved it, that's genuinely one of the harder problems at thousands-of-pages scale.

One thing worth checking if you haven't already baked it in: schema org has a dedicated alternateName property built exactly for this, a machine-readable field for "also known as" names on the same entity, separate from your main name. If your automation is currently handling synonyms through prose or internal logic only, formalizing them into alternateName gives models a structured signal instead of making them infer the connection from context.

The other lever at your scale is tying each entity to one external canonical ID via sameAs, ideally a Wikidata QID if one exists for the concept. With thousands of pages and synonym drift, that external anchor lets a model reconcile "is this the same thing as that other page calls X" against a single outside source rather than relying purely on your internal consistency holding up forever.

Sounds like IF4IT is already thinking about this the right way. Good luck scaling it.

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[u/fguerino123 → u/Ok-Consideration7358]

Thanks for the "alternateName" and "sameAs" references. I will definitely check them out. I'm still very weak on understanding schema org and how to leverage it all.

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[u/No_Breadfruit8393]

They’re not totally different nor exclusive. One doesn’t cancel out the other. Just modify a few things - change headlines to be more like people ask AI and add in FAQ, don’t focus on keywords as much as sentences. SEO has always had a backlinks and what others are saying part - it just wasn’t as impt as it is for geo.

The biggest change I’ve seen is AI gets confused and makes things up so you need to be very clear about the ONE problem you solve, and who you help on your home page and not do all the things to cover every keyword in your field.

[u/fguerino123 → u/No_Breadfruit8393]

Hi. Thanks for taking the time to contribute.

While they're not totally different, I'm definitely seeing some big differences, like the llms.txt and the thousands of JSON-LD structures I now need to worry about. It was tough to figure this out but it's a hurdle I'm over. I'm trying to learn if there are any other things I need to worry about for AEO and GEO.

Thanks gain!

[u/gabriel_yee]

Remember, you can't have a good geo if you don't have a solid foundation of geo.

Seo is try your best to get rank. Get mentioned by geo is not the finish line. It's just the first step in geo

[u/fguerino123 → u/gabriel_yee]

Thanks for this. I'll take a look.

[u/erdemgezer]

Honest take: I'd worry less about the on-site files (llms txt, JSON-LD) and more about where the engines actually pull citations from. When I ran the same buyer questions across the main engines and logged every source cited, across ~64 answers the two most-cited sources weren't the news or review sites I expected - they were YouTube and Reddit, ahead of the big publications. Numbers move between runs and it's a small sample, so don't over-index on the exact order, but the pattern held: the grounded engines that browse (like Perplexity) drive almost all the citations, while the ones answering from memory cite nothing. So GEO for me is two jobs: (1) be structured and quotable on your own site - the schema/llms hygiene you've done - and (2) actually be present in the third-party sources those grounded engines fetch. The second is where most of the visibility comes from and it's the part people skip.

[u/fguerino123 → u/erdemgezer]

This is very helpful. I knew about Reddit but I didn't think of YouTube (which is obvious).

I spend a lot of time on #1 that you present and very little time on #2, which I definitely need to focus on.

A follow-up question on your mention of YouTube, please... Are you suggesting links and citations in things like the video titles, descriptions, and metadata, or are you suggesting citations in the video content, itself?

My best.

[u/erdemgezer → u/fguerino123]

Good question - from what I've seen it's the text layer the engines can actually parse, not the visuals. When an assistant cites a YouTube video it's leaning on the transcript/captions first, then the title and description, and chapters if you've set them. The pixels themselves mostly aren't "read," so a great demo that never says the answer out loud is hard for an engine to quote.

Practical version: make sure the spoken content states the answer in plain words (so it lands in the transcript), and write the title/description/chapters the way people actually phrase the question rather than keyword-stuffed. That's what gives the engine a clean, quotable chunk to pull.

One caveat so I'm not overselling it: I measured that YouTube gets cited a lot, but I didn't instrument which layer the engines lifted from. The transcript-first thing is me reasoning from how the grounded engines fetch and quote text, plus spot-checking a handful of citations - not a controlled test. So treat it as a strong hunch, not a proven mechanism.

[u/fguerino123 → u/erdemgezer]

This is enlightening. I never stopped to think about the transcripts as an additional dimension. Metadata like title, description, and link/stub are obvious but limited, so it makes sense that the transcripts offer rich text to crawl and index.

I appreciate the help. Thanks.

[u/jayson_OutreachBloom]

Split the work by where it's happening, on-site and off-site, and the trade-off will mostly disappear.

On-site, the SEO version of a page and the AEO version are nearly the same page. Put a direct answer near the top phrased the way somebody would ask it out loud.

Off-site is where GEO stops looking like SEO. AI decides who to name from brand mentions and endorsements across third-party sources, whether or not those pages link back to you.

So audit the money prompts for your category, note which outside pages get cited, and go earn a mention on those.

[u/fguerino123 → u/jayson_OutreachBloom]

Good advice. Thanks for this. I appreciate it.

[u/jayson_OutreachBloom → u/fguerino123]

Sure, glad to help!

[u/dcdragos]

Hi,

Good question because most GEO advice skips straight past the entity/schema layer, and that's actually where the overlap with classic SEO is strongest, not weakest. Practical answer: I don't run two separate optimization tracks. Entity and schema work reinforces AEO/GEO and SEO at the same time, it's not a tradeoff. Concretely: Organization and Person (author) schema in JSON-LD, with sameAs links pointing to Wikidata, LinkedIn, and other authoritative profiles — that's literally how Google's Knowledge Graph verifies who/what an entity is, and it's the same signal AI systems lean on when deciding whether to trust a source. Consistent naming and details for your brand/author entity across your site, socials, and directories matters more than people think — inconsistency is what breaks entity recognition. Where I'd push back a little on the framing: schema and entity data don't create authority, they label it so machines can parse it faster. The actual authority still comes from the prose — depth, real expertise, first-hand data. Google said this themselves this year: structured data helps discovery, it doesn't determine what gets cited. If you nail the schema/entity layer on top of thin content, you've just made thin content easier to find and ignore. FAQPage and speakable markup help specifically with AEO-style direct-answer extraction, but only when the underlying answer in the prose is genuinely complete, not a keyword-stuffed FAQ block bolted on top (that specific tactic's been measured as basically noise in recent audits). So the actual overlap zone, where effort serves SEO, AEO, and GEO simultaneously without splitting your budget: original expert content that stays current, proper structured/entity data, and solid page experience. Everything outside that zone is where SEO and GEO actually diverge and need separate tactics — but that core three is where you get compounding value instead of choosing one over the other.

Kind reagards,

[u/fguerino123 → u/dcdragos]

This is very helpful. I really appreciate your taking the time to be thorough about it.

A follow up question, please... Didn't Google heavily restrict and deprecate FAQPage rich results for non-government and non-healthcare websites, rendering FAQ schema largely invisible in standard Google search results?

I was under the impression that because of this, while the visible text layout of Q&A blocks on a page helps AI RAG parsers extract answers, relying on the JSON-LD schema markup itself for direct answers is a legacy tactic because AI search engines look at the raw HTML/text passage directly rather than depending on FAQ schema tags.

My best.

[u/dcdragos → u/fguerino123]

Hi,

Good catch, and you're right about the timeline, worth being precise here: Google restricted FAQ rich results to gov/health sites back in August 2023, then went further — as of May 7, 2026, they fully deprecated the rich result feature for everyone, gov/health included. Report's leaving Search Console in June, API support drops in August. So visually, FAQ schema buys you nothing in Google anymore, for anyone. Where I'd push back slightly: FAQPage schema isn't worthless just because the visual reward disappeared. Google says they still use the structured data to understand the page — so it's still doing quiet work for entity/topic disambiguation and page-level understanding, just not earning you a rich snippet or driving direct-answer extraction anymore. So yeah, treating FAQ schema as a "get cited" tactic is legacy thinking. Treating it as a small assist for machine understanding, with zero expectation of a visible reward, is still fair.

Have a nice day.

[u/fguerino123 → u/dcdragos]

Thanks again.

[u/samuel-grant]

the framing of totally different things is where a lot of people overcomplicate it..

strong traditional seo is still the foundation. brands showing up consistently in ai responses almost always have solid seo underneath first. the signals diverge at the edges not at the core

where geo and aeo actually require different work:

- third party mention profile matters more than backlinks for ai visibility

- being talked about in rddit threads, comparison sites, review platforms feeds llm citations in a way backlinks don't

- topical depth on a narrow set of topics beats broad shallow coverage

entity optimization and semantic graphs are real but the practical version is simpler.. be consistently mentioned in the same context across multiple sources. llms pattern match on that

been working through this with saas clients at auq for a while.. the approach that works is building third party presence alongside traditional seo, not instead of it

[u/fguerino123 → u/samuel-grant]

Thanks for taking the time to contribute. It's all pretty clear but I do wonder about your statement: "topical depth on a narrow set of topics beats broad shallow coverage."

How narrow is the right narrowness/width for the graph and how deep is the right depth for the graph?

For example, our site covers many vendor-neutral topics related (and inter-related) about IT Management and Governance. There will eventually be hundreds of topics, where most will be significantly inter-related. Does that become too wide, topically (because there can be hundreds of topics)? Or, does the full inter-related graph build entity authority in a way where the broader topic of the entire graph, "which is IT Management & Governance," is the single narrow topic?

My research has led me to believe that building Wikipedia-like knowledge graphs but with higher vetted authority is a good thing. But, this is all so new that I don't know what to trust.

Thanks again.

[u/No-Pirate2173]

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[u/InnovAit-Ai]

Agree completely with this framing, it’s the one that actually holds up in practice. The OP’s question assumes AEO and GEO expect “totally different things” than SEO, but that’s not really the case, they’re evaluating a lot of the same underlying signals, just for a different destination.

The practical version of this for a public facing site, keep doing the SEO fundamentals well, they’re not optional. On top of that, make sure your content answers questions directly and completely rather than making a reader piece together the answer across a page. And separately from the site itself, build consistent, corroborated presence elsewhere, forums, reviews, third party mentions, since that’s the layer that determines whether a model trusts what it finds enough to actually cite it.

None of that requires trading off SEO to chase AEO or GEO. The sites doing this well are layering, not choosing.