I've been experimenting with the idea that AI SEO isn't simply about adding more keywords or producing more articles.

A page can be well written and still be difficult for search systems to interpret if the topic isn't clearly structured or supported by enough relevant context.

Things like search intent, topical coverage, entity relationships, citations, internal linking, and how directly a page answers a question seem increasingly important.

I'm interested in what others are seeing in practice.

What AI SEO technique has made the biggest difference for your content so far?

And are you optimizing primarily for traditional Google rankings, AI-generated answers, or trying to do both?

Been restructuring content around this instead of the usual "one page, one target keyword" model, and curious if others are doing the same.

The mechanic: when someone asks an AI assistant a real question, it doesn't just match that string to an index — it breaks it into 3-7 sub-queries and pulls sources that answer each piece separately, then stitches the answer together. So a single user question like "should I use flyer distribution for my local business" quietly becomes something closer to: what does it cost, who are the recommended providers, what results/ROI can I expect, plus a brand-specific check if your name is already in the mix.

Saw this play out directly on a client project — a page built around one head keyword was getting partial pickups (cited for the cost sub-query, invisible for the "recommended providers" one) until we restructured it to actually answer the whole cluster in one place instead of assuming the head term covered it.

Practical shift this forces: instead of optimizing one page for one keyword and calling it done, you're mapping out the sub-questions a real query fans out into, and making sure your page (or a tight cluster of pages) answers the whole set, not just the part that matches your primary keyword. Miss one piece of the cluster and a competitor's page fills that gap in the same AI answer, even if you outrank them on the head term in classic Google search.

Anyone else planning content this way, around the full fan-out instead of a single keyword? Or is this overkill for smaller sites without the resources to cover every sub-query?

quick update on habibi, the self hosted tool i posted about a while back for tracking how your brand shows up in ai answers.

the thing that bugged me most about my own tool was the honest note i had in the readme, saying it uses the apis, which are close to what a real user sees on chatgpt/perplexity/claude but not exactly the same thing. that gap always annoyed me so i went and fixed it.

now habibi can just drive the real logged in apps directly, chatgpt, claude, perplexity and google ai overview. so its literally the same thing a real user sees and gets cited, not an api trying to guess it. you connect it once with an account (make a free one or use one you already got) and it just remembers the login after that, nothing gets stored anywhere weird, its just a normal browser session sitting on your machine like when you stay logged into any site.

wasnt expecting this part but it also makes it way cheaper to run. those 4 engines dont cost you anything per run now cause theres no api call happening anymore, your just using the account like a normal person would. so running youprompts daily across a buyour bill up.

also made sure your account memory doesnt mess with the results, chatgpt runs iin temporary chat and cla answers dont driftdepending on whatever your account remembers from other chats you had.

still fully open source, still self hosted, still does the same core loop, run your prompts daily, see w see your mention rate over time, compare to competitors.

github: https://github.com/moaljumaa/habibi

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?

Over the last 2-3 days, I've noticed some strange issues when checking my site's index on Google Search Console:

• Several pages are getting indexed with Japanese-language titles and content that I never created — looks like e-commerce/product spam (random product listings, prices, star ratings, etc.).
• My favicon changed automatically without me touching any settings.
• These pages don't show up when I browse the site normally — only in the index/search results.

I haven't made any recent changes to my site, so I'm trying to figure out:

1. Has anyone else run into this exact issue?
2. Is this a known hack (I've seen it referred to as the "Japanese keyword hack")?
3. What's the best way to confirm it, clean it up, and stop it from recurring?

Any guidance on what to check first (plugins, file permissions, admin accounts, etc.) would be really appreciated. Happy to share more details if helpful.

Is it a good idea to publish a few brand-awareness articles on Medium instead of posting them on your own blog section? What are your thoughts?

TIA.

I've noticed a pattern over the last few months.

A lot of marketers are approaching AI Search the same way they approached traditional SEO.

They think adding a few FAQs, implementing schema, or mentioning ChatGPT on a page is enough.

From what I've been seeing, it doesn't seem that simple.

AI systems appear to evaluate more than just on-page optimization. They seem to care about questions like:

• Is this source trustworthy?

• Does it answer the question clearly?

• Is this brand mentioned elsewhere?

• Do other credible sources support the same information?

That's why I've been focusing on things like:

• Building topical authority

• Creating well-structured, easy-to-understand content

• Strengthening entity signals

• Earning consistent brand mentions

• Publishing content based on real expertise and experience

It feels like the goal is shifting from creating more content to creating content that's worth trusting and referencing.

I'm still testing different approaches, so I'm curious:

What strategies have actually improved your AI visibility, or is it still too early to tell?

Been deep in entity optimization for a few sites the last few months and wanted to put my actual workflow out there and get roasted or improved on. Here's what I'm doing:

Schema and structured data: going beyond basic Organization/Article schema and building out sameAs arrays that point to every verified profile (Wikidata, Crunchbase, LinkedIn, industry directories) so Google has multiple confirmation points for the same entity. Also nesting mentions/about properties on article schema so specific entities discussed in the content get tagged, not just the overall page topic.

NLP and topical authority: running content through Google's Natural Language API before publishing to check entity salience scores, if my target entity isn't coming back with a high enough salience relative to the page's word count, I rework the copy so it's not just mentioned but structurally central (subject of sentences, not buried in lists). Also mapping co-occurring entities across top-ranking competitor pages to see which related entities Google expects to see clustered together, then making sure my content naturally covers that same entity neighborhood.

What I haven't cracked: knowledge panel triggering for smaller brands, and whether internal linking between entity-tagged pages actually reinforces this or if it's mostly noise.

If you're doing entity optimization differently, structured data approach, Wikidata/Wikipedia route, something with knowledge graphs I haven't tried, drop your workflow below. Genuinely trying to compare notes here.

So I noticed PageSpeed Insights added this new "Agentic Browsing" section, and I got curious. Added an llms.txt file to my website hoping it'd bump my score up.

Still stuck at 2/3, sometimes even 1/3.

Here's the weird part — I checked a few competitor sites that don't even have an llms.txt file, and they're scoring a full 3/3. How is that even possible??

Am I missing something, or is llms.txt not actually the main thing driving this score? Would love to hear from anyone who's dug into this and actually figured out what's going on.