How to rank #1 on ChatGPT?

The citation source breakdown is the most useful part here: Wikipedia for ChatGPT, Reddit for Perplexity and AI Overviews. We saw the same at SocialPost.ai, a couple of Reddit mentions moved our AI visibility more than months of schema cleanup. Do prompt rankings hold once a competitor lands a fresh comparison article, or do they flip within days?

The crawl lag point is real. I've been experimenting with getting my Flutter apps surfaced in AI answers, and the gap between publishing good content and seeing it cited feels like weeks, not days. One thing that helped was making sure the app's landing page answers the exact question in plain text near the top ? not buried in a hero section or behind a CTA. AI engines seem to reward the first clear paragraph more than flashy design. Would be curious if Alex accounts for answer positioning in page structure.

I'm studying also this things, GEO is becoming really important but maybe is still not clear how to get the key of success. I think there are several combinations to rank and get suggested from AI, crawler work differently (gemini, openai etc..)

The crawl lag point is real. I've been experimenting with getting my Flutter apps surfaced in AI answers, and the gap between publishing good content and seeing it cited feels like weeks, not days. One thing that helped was making sure the app's landing page answers the exact question in plain text near the top ? not buried in a hero section or behind a CTA. AI engines seem to reward the first clear paragraph more than flashy design. Would be curious if Alex accounts for answer positioning in page structure.

Solid breakdown — 1 through 5 are the actual fundamentals and it's useful to see them written out plainly rather than as a black box.

The thing I'd add: a single "we ranked #1" screenshot is a sample of one, and these answers are non-deterministic. The metric that matters isn't "did we appear" but "what share of runs cite us" for the same prompt over time — one flattering result and a stable citation rate across 50 runs are very different claims.

Genuinely curious how Alex handles that — does it track citation rate across repeated queries, or point-in-time position? That variance piece feels like where the real proof lives.

Interesting perspective. One thing I've noticed is that it's less about "ranking #1" and more about becoming a consistently citable source. Clear answers, original insights, and authority across multiple platforms seem to matter more than traditional SEO tactics.

Used to spend weeks reaching out to publishers for backlinks . If your AI solves that, it's a game changer.

The thing that surprised me when I actually measured this: the traffic is real, the volume is small, and the intent is very high.

Over 90 days ChatGPT sent us 16 visitors and 30 pageviews. Tiny. But in the period I checked it was one of only two channels that produced any signups at all, while classic social produced zero. So the ROI per visitor is nothing like the ROI per visitor from a feed.

What seemed to move it was not a schema tweak. It was having the direct answer to the exact question inside the first two sentences, then a real number with a source attached. The pages that got cited were the ones where the answer was extractable without reading the whole article.

One practical thing: split it out from Google in your analytics rather than leaving it blended under organic. Blended, 16 visitors disappears into the noise and you conclude it does nothing.

This is great. Thank you for contents like this 🫡

The backlink point tracks with what I've seen, but I'd push back a little on the "get cited by the sources already ranking your competitors" step being purely a content quality problem. A lot of what shows up in AI Overviews for B2B-ish queries is old Reddit threads and Quora answers that never got updated, not necessarily the best current source, just the one the model already trusts and has indexed. There's a lag between "good content exists" and "the model actually surfaces it" that seems more about crawl and citation timing than about the content itself.

Curious whether Alex accounts for that lag somehow or just flags the gap and you're stuck waiting for the next crawl cycle. That's been the more frustrating part on my end than the content or backlink work itself.

Great breakdown. I’ve been testing AI workflows for content creation, and one thing I’ve learned is that distribution matters much more than prompt quality. Thanks for sharing your experience.

Building on mocktomer’s point that none of this shows up in analytics, because I think it is the most solvable part and the least discussed: you cannot measure citations in analytics, but you can measure them in server logs.

Disclosure, I work on growth at Ojin and we track this for our own site.

Assistant fetchers identify themselves by user agent, and the distinction that usually gets collapsed is between training crawlers and live retrieval fetchers. GPTBot is largely corpus collection. OAI-SearchBot and PerplexityBot fire when a person has actually asked something and the engine goes out to retrieve. So a hit from the second group is a leading indicator that a specific page entered the retrieval path for a live query, often before any citation is visible to you. That signal is free, already sitting in your logs, and it does not depend on anyone clicking through, which is exactly the gap mocktomer described.

It also makes a strategic choice available that has not come up here: those user agents can be handled separately in robots.txt. You can decline the training crawlers while staying fully available to the retrieval fetchers, so you remain in the answers without feeding the next model. Whether you want that is a judgment call, but most people do not realise it is a separable decision at all.

And on Korelyy’s question about whether FAQ or HowTo schema actually moves citations or whether the content structure is doing the work: worth knowing Google deprecated FAQ rich results for most sites back in 2023, so that markup no longer earns a SERP feature. What it still does is force you to write in question and direct-answer pairs, which is the shape that gets extracted. So the honest answer is probably that the structure is doing the work, and the schema is a useful discipline for producing it rather than a ranking input in its own right.

​That shift toward how AI search engines choose their sources is fascinating. As mentioned here, there is a massive difference between what triggers a citation in ChatGPT versus Perplexity or Google AI Overviews.
​I am currently building out an independent e-commerce and software ecosystem where we are looking quite a bit at how to automate optimization and skip the manual grunt work. It's super interesting to see how tools like Alex are trying to solve it.
​Anyone else here experimenting with structuring their site specifically for AEO (AI Engine Optimization) yet, or are you still sticking purely to traditional SEO?

This was really helpful guide which triggered me to try the service. Nice to see people sharing their findings in details

Been deep in this exact rabbit hole — I test how AI assistants see websites, and I ran the check on my own site first: scored 33/100, visibility literally 0. What actually moved the needle in testing: (1) machine-readable product data — schemadotorg Product/Offer markup, real prices in HTML instead of baked into images; (2) an llms.txt plus clean category pages crawlers can parse; (3) making sure key pages render without JavaScript — most assistant fetchers don't execute it; (4) being present in places assistants already trust: directories, comparison pages, and review sites get cited constantly in their answers. The uncomfortable part: none of this shows up in analytics, because the shopper who asked ChatGPT never visits your site at all. You lose them before the click exists.

The schema + crawlability piece is underrated - most people jump straight to backlinks and skip step 1 entirely. One thing I have noticed running a tools site: AI engines seem to cite pages that answer one specific question cleanly far more than long "ultimate guide" pages, even when the guide has more authority behind it. Direct-answer blocks and clear question-style H2s get pulled into ChatGPT/Perplexity summaries much more often for me. Have you seen structured data like FAQ or HowTo schema actually move citations, or is it mostly the content structure doing the work?

the Reddit signal is the one most people underestimate. when we looked at where ChatGPT was pulling from for AI-related queries while building aisa.to, community threads and niche discussion posts showed up way more than traditional blog content. schema and structured data help with crawlability, but being genuinely cited in conversations where people are already asking questions is what actually moves rankings.

It's interesting to see how your platform helps for AI visibility search. gonna try it!

It's interesting to see some much of the fundamentals between SEO and AEO is the same. I like the breakdown in the report . Although some parts are quite different that what I expected.

getting blog mention for my business was a huge pain, if your AI finds publishers and reach out to get backlinks that is a huge win .. let me try the free visibility

thanks for sharing - I wonder how long it takes to get similar results

I like the breakdown and the structure make sense, but how the backlink works, like your AI agents find publishers that would give my business a backlink or there is a directory we pick from?

I think the biggest advantage of this system is the backlink building and fresh content - website audit is foundational so if someone doest do that thy probably not even selling anything ...

def love the itemized plan - it was under the impression wikipedia is not a reliable source , I am SURPRISED to see it is the top source for chatgpt!!!! how many prompts did you analysis for this?