We all know that AI assistants like ChatGPT entry search indices, like Google and Bing, to retrieve URLs for his or her response. However how, precisely?
To seek out out, we’ve run a collection of experiments wanting on the relationship between the URLs cited by AI assistants, and the outcomes present in Google when looking for a similar matters.
To date, we’ve examined long-tail prompts (very lengthy, very particular queries similar to these you’d enter into ChatGPT); fan-out queries (mid-length prompts that relate to the unique long-tail immediate); and right now we’re testing short-tail key phrases—ultra-short, ultra-specific “head” phrases.
Brief-tail key phrases provide the clearest illustration of how AI citations observe with Google outcomes.
Primarily based on three separate research, our conclusion is that ChatGPT (and comparable techniques) don’t simply elevate URLs straight from Google, Bing, or different indexes. As an alternative, they apply extra processing steps earlier than citing sources.
Even once we examined fan-out queries—the precise search prompts these techniques ship to serps—the overlap between AI and search engine citations was surprisingly low.
In different phrases, whereas ChatGPT could pull from Google’s search index, it nonetheless seems to use its personal choice layer that filters and reshuffles which hyperlinks seem.
It’s due to this fact not sufficient to determine fan-out queries and rank properly for them—there are extra components influencing which URLs get surfaced, which can be outdoors of a writer’s management.
Completely different question sorts inform us various things about how AI assistants deal with info.
In our earlier analysis, Ahrefs’ information scientist Xibeijia Guan analyzed quotation overlap between AI and search outcomes for informational long-tail and fan-out prompts, utilizing Ahrefs Model Radar.
This time, she has taken a pattern of three,311 traditional Website positioning-style head phrases, protecting informational, business, transactional, and navigational intent.
| Instance question | Informational | Business | Transactional | Navigational |
|---|---|---|---|---|
| 1 | cincinnati bearcats basketball | greatest bank card rewards | swimming pools for sale | onedrive signal in |
| 2 | protein in shrimp | soundbar for television | store ladies costume | verizon buyer help |
| 3 | what’s cybersecurity | at dwelling sauna | purchase a website | costco rest room paper |
Every key phrase has been run by way of ChatGPT, Perplexity, and Google’s high 100 SERPs to research quotation overlap between AI and search.
If something have been to align carefully with Google’s outcomes, you’d count on it to be short-tail queries—since that’s the traditional means we search.
However that’s not fairly the case.
Whereas the quotation overlap for short-tail queries (10%) is barely stronger than for fan-out queries (6.82%), it’s nonetheless a lot weaker than we’d count on if it have been straight echoing the SERPs.
That is much more stunning, now we’ve affirmation that OpenAI and Perplexity have been scraping Google outcomes through a third-party supplier.
It’s doable we’d see extra overlap if our research centered solely on ‘real-time’ queries (e.g., information, sports activities, finance), since these are reportedly the varieties ChatGPT scrapes Google for.
Perplexity citations align carefully with Google’s search outcomes throughout short-tail queries.
Not like ChatGPT, overlap isn’t simply seen on the area stage—most of Perplexity’s cited pages are additionally the precise URLs rating in Google’s high 10.
This mirrors the findings in our long-tail question research, the place Perplexity responses most resembled Google’s outcomes, reinforcing its design as a “citation-first” engine.
Area overlap is persistently increased than URL overlap, suggesting that ChatGPT and Perplexity cite the identical web sites as Google—however not the very same pages.
In ChatGPT, the domain-URL hole is particularly large—31.8% vs. 10%.
In different phrases, ChatGPT cites rating domains ~3X greater than rating pages.
On the one hand, this might imply ChatGPT selects totally different pages from the similar domains as Google.
For instance, Google cites one web page from ahrefs.com/writing-tools/, whereas ChatGPT finds a greater “match” on ahrefs.com/weblog/ and cites one other.
If true, this reinforces the worth of making cluster content material—optimizing a number of pages for various subject intents, to have the most effective likelihood of being discovered.
One other risk is that each lean on the identical pool of authoritative domains, however disagree on arbitrary pages.
Assess your cluster content material in AI and search
You possibly can verify the Website positioning efficiency of your cluster content material within the Associated Phrases report in Ahrefs Key phrases Explorer.
This may present you if and the place you rank throughout a complete cluster of associated key phrases.
Simply add a Mother or father Subject filter, and a Goal filter containing your area.
When you’ve finished that, head to Ahrefs Model Radar to verify on the AI efficiency of your cluster content material.
Run particular person URLs by way of the Cited Pages report in Ahrefs Model Radar to see in case your cluster content material is being cited by AI assistants like ChatGPT, Perplexity, Gemini, and Copilot.
Work out if any content material is lacking from both floor, then optimize till you’ve crammed these gaps and enriched the general cluster.
You should utilize subject hole suggestions in Ahrefs’ AI Content material Helper to assist with this.
Brief-tail queries present nearer SERP-AI alignment than pure language prompts—particularly with regards to Perplexity.
However the ChatGPT citations generated by fan-out queries (first studied by SQ and Xibeijia) present the least overlap. They match solely 6.82% of Google’s high 10 outcomes.
We’re not evaluating apples-with-apples right here. These percentages symbolize totally different research, and totally different sized datasets.
However every research produces comparable findings: the pages that ChatGPT cites don’t overlap considerably with the pages that Google ranks. And it’s largely the alternative for Perplexity.
One different factor we haven’t talked about is intent. The larger quotation overlap we see throughout short-tail queries may partly be defined by the relative stability of navigational, business, and transactional queries—which we didn’t assess in our earlier research.
Navigational, business, and transactional head phrases have SERPs that don’t have a tendency to alter too typically, as a result of the set of related merchandise, manufacturers, or locations is finite.
This stability means AI assistants and Google usually tend to converge on the identical sources, that means overlap is increased than it’s for informational queries (the place the pool of doable pages is way bigger and extra risky).
Remaining ideas
Throughout all three research, the story is constant: ChatGPT doesn’t observe Google’s sources, Perplexity does.
What’s stunning is that ChatGPT differs a lot from Google, once we now know that OpenAI does scrape Google’s outcomes.
My hunch is that ChatGPT does greater than Perplexity to distinguish its outcomes set from Google.
This idea from SQ appears essentially the most possible one to me:
“ChatGPT doubtless makes use of a hybrid strategy the place they retrieve search outcomes from varied sources, e.g. Google SERPs, Bing SERPs, their very own index, and third-party search APIs, after which mix all of the URLs and apply their very own re-ranking algorithm.”
Regardless of the case, search and AI are shaping discovery side-by-side, and the most effective technique is to construct content material that provides you an opportunity to seem on each surfaces.
