Getting Listed on Chat GPT

TL;DR

Getting cited by ChatGPT and other AI search tools takes more than ranking well on Google — it means auditing where you're currently invisible in AI answers, keeping your brand's naming consistent everywhere, backing it up with entity-focused schema, and writing content in a scannable, question-led format that's easy to extract. Layer in real trust signals, quotable FAQs, and a clean internal linking structure so AI models can verify and connect who you are, then treat sentiment tracking and competitor citation analysis as ongoing habits rather than one-time checks. None of it replaces traditional SEO — it's the same content discipline, just built for one more audience, and it only works if you keep testing and refreshing it monthly.

Post 1/10: I stopped assuming Google rankings were the whole story

For years, "are we ranking?" was the only SEO question I asked. Then I started noticing something: pages that rank fine on Google are showing up nowhere when someone asks ChatGPT the same question. Different game, different scoreboard.

So I ran what's basically an AI visibility audit — I took the questions our customers actually ask, fed them into ChatGPT and a couple of other AI search tools, and watched who got cited. Sometimes it was us. A lot of the time it was a competitor, or worse, some random third-party blog neither of us had heard of.

The takeaway: rankings and citations are two different currencies now. You can be winning one and be invisible in the other. Before you touch a single page, find out where you actually stand in AI answers — not where you assume you stand.

Next up: once you know where you're invisible, the fix starts with something surprisingly unglamorous — making sure AI models actually know what you are.

Post 2/10: AI doesn't know what your product is unless you tell it, everywhere, the same way

Here's a dumb mistake I was making: our product name was written four slightly different ways across our own website, our app store listing, and a directory listing from two years ago. To a human, obviously the same company. To a language model stitching together "who is this brand," it's noise.

This is the unsexy part of AI SEO — entity mapping. Basically: pick the exact names for your core things (your product, your founders, your categories) and use them identically everywhere, from your homepage to your Google Business Profile to every directory you're listed in.

It sounds too simple to matter. It's not. AI models build their understanding of "what you are" out of these consistent signals across the web, not just from your own site.

Next: once your entities are consistent, there's a technical step that turns that consistency into something machines can actually parse — structured data.

Post 3/10: The three lines of code that apparently 3-5x your odds of getting cited

I'll be honest, schema markup always felt like busywork. Add some invisible code, hope Google notices. But the number that got my attention: sites with proper entity-focused schema see 3 to 5 times higher odds of being cited by AI systems. That's not a rounding error.

The move is to go beyond basic Article schema and actually mark up your entities — Organization schema with your real name, logo, and socials, Product schema for what you sell, Person schema for your team. It's how you hand AI models a labeled diagram of your business instead of making them guess from prose.

We're mid-way through closing this gap on our own book pages right now, and it's the single most concrete item on this whole list — you can point at a checkbox and know it's done.

Next: once the metadata is right, the content itself needs to be shaped differently too.

Post 4/10: Write for the 8-second extraction, not the 8-minute read

I used to write blog intros the way I was taught — set the scene, build up, get to the point in paragraph four. Turns out that's exactly the structure AI systems struggle to extract from.

The fix is almost the opposite of "good writing" as I learned it: short paragraphs, real headers (bonus points if they're phrased as questions, since that's literally how people prompt AI), bullet points, and the answer stated plainly near the top instead of buried under throat-clearing.

It's a genuinely useful discipline even for human readers — nobody's mad about getting to the point faster. But for AI extraction specifically, a clean, scannable structure is the difference between your page being the source and your page being skipped over for something easier to lift a quote from.

Next: structure gets you extracted. The next tip is about getting trusted enough to be extracted first.

Post 5/10: Trust signals aren't a "nice to have" anymore, they're a ranking input

I always thought of things like author bios, case studies, and reviews as credibility theater — nice for humans, irrelevant to a crawler. That assumption was wrong.

AI systems are trying to figure out who's a reliable source before they'll cite you, the same way a person would size up a stranger's advice. Real bylines with real credentials, actual customer data, case studies, awards, and links from other trusted sites all function as evidence, not decoration.

The practical version of this: don't just publish content, attach a human to it. Put a name and a bio on it. Back claims with your own data where you have it. It's slower than firing off anonymous posts, but it's the difference between "content" and a "source."

Next: one of the most direct ways to hand AI models exactly the trust-building answer they're looking for — FAQs, done right.

Post 6/10: Answer the question in one sentence, then explain yourself

FAQs used to feel like an SEO afterthought — a section you bolt onto the bottom of a page to catch a few extra keywords. Reframe them: they're the single easiest place on your site for an AI system to lift a clean, quotable answer.

The format that works: start the answer with a direct position — yes, no, usually, it depends — in under 40 words, then explain. Cover four to eight real questions people actually ask, and don't be shy about repeating FAQ blocks throughout a longer piece, not just at the bottom.

It's a small format change that does a lot of work. You're not writing differently to please a robot, you're just finally answering the question instead of dancing around it for three paragraphs first.

Next: your best FAQ answer still won't get found if it's sitting on an orphaned page nobody links to. Time to talk about internal linking.

Post 7/10: I found pages on our own site that nothing else linked to

Not a great feeling — realizing there were pages on our own site that no other page pointed to. If your own site doesn't think a page is important enough to link to, why would an AI model think it's worth citing?

The fix: identify your real pillar pages — the handful of topics you actually want to be known for — and make sure every related piece of content links back to them, using anchor text that actually describes what's being linked, not "click here."

Think of it as drawing the map for the AI model, showing it which pages relate to which. A tight, logical link structure is one of the cheapest, fastest technical fixes on this entire list, and it also just makes your site better for humans navigating it.

Next: once the structure's fixed, the real work becomes ongoing — figuring out not just if you're cited, but how.

Post 8/10: Getting cited isn't the finish line — how you're described is

This one surprised me. I assumed "we got mentioned" was the win condition. It's not. A citation that frames you negatively, or gets a key fact wrong, can do more damage than not being mentioned at all.

So now sentiment tracking is a regular habit, not a one-off check — going back monthly to see not just whether we show up in AI answers, but how we're described when we do. Certain product details getting consistently mischaracterized is a real pattern worth catching early, before it hardens into "the thing AI says about you."

If you only take one thing from this whole series, it's this: visibility without sentiment tracking is flying blind. Being mentioned and being mentioned well are different goals.

Next: you can't fix your own gaps until you know exactly where competitors are winning that you aren't.

Post 9/10: I started keeping a running list of who's beating us — and where

Competitor research used to mean glancing at their pricing page. Now it means literally asking AI tools the questions our customers ask and writing down who gets cited instead of us, for which specific topics, and in what format — listicle, comparison table, deep guide.

The interesting part isn't "they're winning." It's where they're winning. Once you see the exact gap — a topic they own and you don't even have a page for — it turns into a very short, very specific content to-do list instead of a vague sense of falling behind.

This is the one I'd recommend doing first if you're short on time. It tells you exactly where to point everything else on this list.

Next, the final tip: none of this matters if you never check whether your changes actually worked.

Post 10/10: Ship it, then go ask the robots what they think

Here's the step I used to skip entirely: after publishing an update, actually going back and testing it. Prompting AI tools with five or ten variations of the target question, a couple of days after publishing, and checking whether the update shows up — and how it's framed.

Content freshness matters more than I expected here too — a large share of what AI systems cite was updated within the past year, so "publish once and forget it" is quietly costing visibility over time.

The loop that actually works: publish, test within a few days, compare before-and-after citation rates, repeat monthly. It's the same discipline as any other growth channel — nothing here works as a one-time project, it works as a habit.

That's the whole ten. If you've been doing traditional SEO and ignoring this, the good news is none of it replaces what you're already doing — it's the same content, just built to be readable by one more audience.