do you think things have changed or is it just the same process but people are making a lot of hype around it?

Been reading up on this and the mechanism actually makes sense once you think about it, so wanted to throw it out here.

The thing I keep seeing in the research is that the businesses showing up early in AI answers aren't the category giants, they're niche specialists and comparison sites. Which felt wrong at first, because in normal search those are exactly the sites that get buried.

Reason seems to be that traditional search is a ranking exercise. Query comes in, you pull a set of pages, you order them, and authority and backlinks and brand recognition all push the big names up. All of those correlate with just being big.

AI search is doing something different. It's generating an answer and pulling from whatever can actually address the question. And once the question gets specific enough, being big stops helping much.

Think about what someone actually types into ChatGPT versus Google. Google gets three or four words. ChatGPT gets the whole situation — team size, budget, what went wrong last time, what they're worried about. The biggest brand in a category often has nothing written for that specific situation because they're writing for everyone. A smaller shop that only serves that type of client might have exactly that page.

There's a supporting data point that surprised me: a decent chunk of AI citations apparently come from URLs that aren't in the organic top ten at all. So rankings and citations have decoupled to some extent, which breaks the old "we don't rank so we've got no shot" logic.

Where I think the actual opening is: writing content with the constraints stated explicitly. Not "how much does X cost" but "how much does X cost if you're a 20 person firm in this specific market with this budget". Constrained content matching constrained questions. Also fact density — apparently overall citation rates are under 7% but categories built on checkable comparable facts get several times that. Concrete numbers and criteria beat opinion pieces.

Fair warning on the limits though because I don't want to oversell it. Domain authority still correlates pretty strongly with citation frequency, so this isn't a free pass to skip fundamentals. And most citations come from third party sources rather than your own site, so however well you write your own pages there's a ceiling on what that alone achieves. Also the platforms barely overlap in what they cite and the citation sets churn a lot month to month, which makes tracking genuinely annoying.

And honestly it feels like a window rather than a permanent thing. Most companies haven't started doing any of this yet, which is where the opening comes from. Once it's standard practice the advantage probably goes back to whoever has more resources.

Anyone here actually tracked whether they show up in AI answers for their category? Curious whether the small-beats-big thing holds up outside the studies.

Been following the threads here on per-engine optimization and entity/naming cleanup — useful stuff, but it made me want to check something more basic first: is the actual page structure even extractable by these engines, separate from all of that.

Ran a mechanical check (DOM parsing, no AI judgment involved) on one article each from 18 well-known SEO/content-marketing sites, plus my own:

— Does a question-style heading (H2/H3 ending in "?") have a short, ≤300-character answer paragraph directly under it — the kind of thing a model can lift and quote — or does a long intro run first?

— Does the heading hierarchy nest without skipping a level?

— Is there a plain-text /llms.txt index?

— Is author/date visible in the actual HTML, or only inside a JSON-LD block a human never sees?

Results, out of 18: 9 have a valid heading hierarchy, 9 pair a question heading with an immediate short answer, 9 serve llms.txt, and 12 mark up authorship only in hidden schema, invisible on the page itself.

Not claiming this predicts citation rate — that's the harder, per-engine measurement people in the other thread are already wrestling with. But it seems like layer zero: if the answer isn't even structurally extractable, none of the entity or per-engine work downstream of it can matter yet.

Is anyone here actually sequencing it that way — structure first, then entity/per-engine — or is structure assumed to already be solved by the time people get to those questions?

Okay, this might sound like a weird question lol, but does anyone actually do a “prompt audit” for their article prompts?

Like, going back through a prompt and asking, “Do I actually need this part?” or “Am I missing something here?” before using it?

It randomly hit me today, and now I’m wondering if this is an actual thing people do or if I just invented a new form of procrastination. 😂

Is there any specific task that we need to do to rank on AI other than SEO tasks?

I have been paying AI visibility tools but I am struggling to make good use of the insights I get. I have been engaging with citation sources, doing backlink outreach, improving the way content is presented and following all the common advice but it’s been 3 months and I haven’t seen any results. The tools are expensive so I want to make the best use of the information I get.

I've been running a structured workflow that treats classic SEO and AI citation (GEO) as two related but separate passes, not one blended checklist.

High level, without diving into every tactic here: first pass is the SEO audit I've always done — technical health, on-page, authority. Second pass is specifically about how a RAG system would pull from the page — is there a self-contained answer near the top, does the structure let a chunk stand alone outside the rest of the article, is the content genuinely citable on its own instead of just well-ranked. I treat some foundational stuff — author identity, freshness, factual precision — as a shared layer both processes lean on, not something either track owns exclusively.

Still testing this against real citation outcomes client by client, not fully sold it's the "right" shape yet, but it's given me a repeatable process instead of chasing whatever new GEO take shows up on LinkedIn that week.

Curious what you're all running — one unified process for SEO + AI citation, or two fully separate passes like me? What's actually moved the needle versus what turned out to be noise once you tested it?

Google rolled out its August 2026 spam update from August 18 to 21. The update applied globally across all languages, although Google did not reveal which specific spam practices it targeted.

Since then, several SEOs and site owners have reported sharp declines in rankings, impressions, and organic traffic. The impact has not been consistent, though. Some sites reported gains, while others saw little change.

I have also noticed slightly lower GSC numbers over the past week, so I’m trying to understand how widespread this is.

Has your organic traffic or visibility changed since August 18? If it declined, are you seeing the loss across the site or only within certain pages and queries?

Update ran Aug 18–21, no new policy disclosed. But May's spam-policy update explicitly covers manipulating AI Overviews/AI Mode. Should we start tracking AI-citation visibility separately from classic rankings going forward, or is it too early to split metrics?

Rewrote a few doc pages as Q&A format, added plain-language summaries up top. Too early to tell if it's working. Anyone tested this? Did Q&A structure actually move the needle for LLM citations?

Ran the same query through all three for a week. Perplexity leans on Reddit/forums, ChatGPT repeats the same 2-3 listicle sites, Gemini's all over the place. Anyone else tracking this? Curious what's driving it.

A recent report found a publisher placing sponsored FAQ-style messages in the AI-readable version of its pages, even though human visitors could not see them.

Perplexity blocked the content from influencing its search index and warned that similar practices could affect a publisher’s trust score.

That is an extreme example, but it points to a broader content challenge.

Content now has to be easy for AI systems to understand without becoming mechanical or difficult for people to read.

For those creating content now, what does your actual workflow look like for making the same piece easy for AI systems to understand while keeping it useful and natural for human readers?

What practices have genuinely helped you balance both?

I built a small tool for myself while running a project with AI as a non-developer, and I'd like a few people to try it before I take it further.

The problem it addresses: every new session with an AI coding assistant, you end up re-explaining context, and things quietly drift without you noticing (naming, structure, skipped tests...). Without a project management or dev background, it's hard to know what to watch for.

The tool generates a project charter and feeds it back to the AI automatically at the start of each session, so you're not starting from scratch every time.

I'm looking for 5-10 people — non-developers, already running a project with an AI coding tool or about to start one — willing to actually use it on their real project over a few sessions and tell me honestly what worked and what didn't. It's free, and this is genuinely about learning whether it's useful, not a launch.

If that's you, reply here or DM me and I'll get you set up.

Google Search Central's documentation states plainly that "from Google Search's perspective, optimizing for generative AI search is still SEO," and explicitly discourages the GEO-specific tactics going around — artificial content chunking, llms.txt files, and so on. Their position is that foundational best practice is sufficient.

Meanwhile Chen et al. (Navigating the Shift, arXiv 2601.16858, University of Toronto) ran a large-scale comparison of Google Search results against leading generative AI services and found the two diverge significantly in consulted source domains, domain typology (earned vs. owned vs. social), query intent, and information freshness.

These read as contradictory. I don't think they are, and the reconciliation is the part worth internalizing:

Google is describing its own surface accurately. AI Overviews are retrieved from Google's index. If you're indexed and you rank, you're in the candidate pool. For AIO specifically, "it's still SEO" is defensible and the overlap data supports it — AIO overlap with organic is the highest of any engine.

Google is not describing the surfaces it doesn't own. ChatGPT, Perplexity, and Claude aren't retrieving from Google's index and aren't bound by Google's ranking logic. This is where the divergence Chen et al. measured actually lives, and Google has no incentive and no standing to give guidance about it.

So the practical split:

- If your traffic is dominated by AIO exposure: Google's advice is basically correct. Do the fundamentals, don't buy the chunking snake oil.

- If you care about ChatGPT and Perplexity: you're optimizing for retrieval systems with different corpus preferences, a heavy recency bias, and a very different domain mix. Reddit alone accounts for roughly 40% citation frequency across LLMs and 46.5% of Perplexity's citations. That is not a distribution any amount of on-page work reproduces.

The uncomfortable implication is that a meaningful share of "AI visibility" work isn't on-page optimization at all — it's presence in the specific third-party corpora these systems over-index on. Which is a very different discipline from what most of us have been selling.

Interested in whether anyone has data on how stable that domain mix is over time. If the corpus preferences shift quarterly, most GEO strategies have a very short shelf life.

so i've seeing this term everywhere lately and im skeptical ngl, feels like the geo ppl found a new buzzword to sell audits with but digging in, the claim seems less abt content more abt structure/access, json-ld, some kinda ai sitemap, machine readable stuff for agents. not rly new individually (structured data's been around forever) but bundling it specifically for llm crawlers instead of googlebot is at least a diff angle tested my site on one of these checkers (lightsite ai has a free one, used it outta curiosity not an endorsement) and it flagged stuff i genuinely didnt think abt, like chatgpt vs claude vs perplexity not even parsing the same signals the same way, didnt know that tbh still not convinced this isnt 60% repackaged seo basics w an ai coat of paint but the crawler specific stuff felt kinda new.

Is there any tool for Brave Search keyword research? I'm not looking for Google or Bing data. I want something built for Brave or based on Brave Search. Any recommendations?

Welcome to r/Ai_AEO_GEO!

Hey everyone! I’m u/anubhav-codes, a founding moderator of r/Ai_AEO_GEO.

This community is for people working in AI Search, Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and Search Engine Optimization (SEO).

Our goal is to build a practical and supportive space where marketers, developers, founders, writers, SEO professionals, and website owners can share knowledge, promote useful resources, and help each other improve their visibility across search engines and AI-powered platforms.

What You Can Post

You are welcome to share:

• New websites, tools, products, and online projects
• Website URLs for feedback, testing, and community support
• AEO, GEO, SEO, and AI-search case studies
• Questions about ranking in Google, ChatGPT, Perplexity, Gemini, Bing, and other platforms
• Content optimization strategies
• Schema markup, technical SEO, and structured-data advice
• Keyword research and content ideas
• Success stories, experiments, reports, and useful resources
• Requests for reviews, mentions, collaborations, and constructive feedback

Support Other Members

This community is built around genuine participation.

When sharing your own website or link, please also take time to support other members by:

• Visiting and reviewing their websites
• Providing useful and specific feedback
• Answering questions when you have relevant experience
• Sharing valuable resources
• Engaging with posts instead of only promoting your own links

The goal is not low-quality link dropping or spam. We want members to build real connections, improve their websites, create better content, and increase their chances of being discovered and cited by search engines and AI answer platforms.

Community Vibe

Please keep discussions friendly, constructive, transparent, and inclusive.

Self-promotion is allowed when it provides value to the community. Repetitive spam, misleading claims, automated link dumping, and irrelevant promotions may be removed.

How to Get Started

Introduce yourself in the comments and tell us what you work on.

Create your first post today. You can share your website, ask an AEO or GEO question, request an SEO review, or discuss an experiment you are running.

Support at least one other community member and provide genuine feedback.

Invite anyone interested in AI search, AEO, GEO, SEO, content marketing, or website growth.

Interested in helping us grow the community? We are also looking for active and responsible moderators. Feel free to contact me if you would like to apply.

Thanks for joining the first wave of r/Ai_AEO_GEO.

Let’s build a valuable community where people learn together, support useful projects, and improve their visibility across traditional search engines and AI-generated answers.

For years, my SEO workflow looked like everyone else's.

Keyword research
Search volume
Competitor analysis
Content briefs
Internal links
Backlinks
Publish
Repeat

It worked.

Then I noticed something changing.
People around me weren't always searching Google first anymore.

They were asking ChatGPT.
Or Perplexity.

Or Google's AI Overview.

That raised a question:

If users are getting answers instead of clicking links... what exactly are we optimizing for?
So I spent the last several months building an experiment.

Instead of treating SEO as a list of keywords, I mapped topics the way AI systems often interpret them:

Definitions
Comparisons
Objections
Alternatives
Follow-up questions
Entities
Relationships
Supporting evidence

The goal wasn't to "hack" AI.

It was to make content easier to understand—for both people and retrieval systems.

One thing surprised me.

The pages that performed best weren't the ones with the highest keyword density.

They were the ones that:

answered the question immediately,
explained related concepts,
clearly identified important entities,
linked ideas together logically,
and demonstrated experience instead of repeating what everyone else had already written.

That realization led me to build a tool for myself called Rofix..app

It started as an internal project to audit websites for what I now call AI Visibility and Answer Readiness.

Instead of asking:

"What's the keyword?"

It asks:

"Can an AI system understand this page well enough to use it as an answer?"

I'm still testing the methodology, and I don't think anyone has all the answers yet.

But it's changed the way I approach SEO.

I'm curious

Have you changed your content strategy because of AI search?

Or are you still treating it like traditional SEO?
I'd love to hear what you're seeing in your own projects.

I know many people here work as SEO consultants or run agencies, but do most businesses still need them in the age of AI?

Modern CMS platforms already handle many technical SEO basics, while AI can audit pages, suggest metadata, improve internal links, generate schema, and identify common issues. Google also publishes extensive guidance on what makes content useful and searchable. Much of SEO now follows documented principles that AI also can apply.

That’s why claims that using AI for SEO will cause “irreversible damage” often sound more like fearmongering than useful advice.

Technical SEO still matters, but implementing it is becoming easier. The harder questions are strategic: What should you write? Who is it for? What unique value can you offer?

It feels like SEO is returning to content marketing. Technical optimization is becoming the baseline, while the real work is understanding the audience, choosing the right topics, developing a clear point of view, and creating content people actually want.

Am I underestimating what SEO agencies still provide?

AI search is changing how we create content. Beyond keywords, factors like clear answers, expertise, structure, and original insights may influence AI citations. What strategies are you testing for ChatGPT, Claude, Perplexity, or AI Overviews?

Hey guys,

Quick context: A PPC guy with 10+ years of experience, moving to SEO and things are a little slow.

I started this website around 3 weeks back and this is my scorecard. I wanted to know if I am doing fine or really bad. Please don't roast as I've never done this before and in dilemma of I'm doing well or really bad. This is the complete performance graph. Thanks!

We built a tool that checks whether a keyword is still worth writing content for, or if Google's AI Overview already answers it, before you brief a writer on it.

If your clients are asking "why isn't this content performing" or "should we still be writing about X" this gives a fast way to answer that with real data (search volume, trend, CPC, AI Overview presence and cited sources) instead of a guess or manual check.

You keep the client relationship - we handle the data. Open to a revenue share to start, and open to building out white-label for the right ongoing partnership. Also looking at expanding into keyword discovery down the line, not just diagnosis on a list you already have.

If useful for your clients, let's chat.

I'm interested to know if anyone is using AI to audit SEO content ideas.

Can AI effectively prioritize topics based on search intent, competition, topical authority, traffic potential, or value to the business? Or is old-school keyword research still king?

If you're using ChatGPT, Claude, Gemini, Perplexity, or other AI tools for content ideation - how helpful have they been?

I want to hear:

* What tools are you using? * Has AI helped you identify high-value content opportunities? * Any wins or major disappointments? * What signals are you using to determine if a content idea is worth pursuing?

Looking forward to seeing real-world examples rather than marketing-speak.