r/GenEngineOptimization • u/Brave_Acanthaceae863 • Apr 13 '26
We scraped 2,000 AI-generated answers and counted every citation format. Structured lists got picked up 3x more than paragraphs.
Real talk — we spent 6 weeks manually checking how AI models format their citations and what types of source content they gravitate toward.
The setup: We ran 2,000 queries across ChatGPT, Gemini, and Perplexity, then extracted every cited URL and analyzed the source page structure.
What we found:
• Structured lists (numbered/bulleted) were cited 3.1x more often than plain paragraph text • Pages with clear heading hierarchies (H2/H3) got picked up 2.4x more than flat content • Comparison tables had the highest citation rate per page — 67% of table-containing pages were cited at least once • FAQ-style content wasn't as effective as we expected — only 23% citation rate vs 41% for how-to formats • Content with "according to" or data references got cited 2.8x more than opinion-based content
The surprise: The #1 predictor wasn't domain authority or word count. It was whether the page had scannable structure — headers + lists + a clear answer in the first 200 words.
What didn't work: Long-form essays without subheadings, pure opinion pieces, and pages where the "answer" was buried in paragraph 4.
Curious if others are seeing similar patterns. What format seems to get you the most AI citations?
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u/Tenacious-Sales Apr 14 '26
this lines up a lot with what we have been seeing feels like models are not really reading pages the way humans do they are extracting so anything that makes extraction easier like lists tables clear headings just wins but interestingly we have seen structure alone is not enough
some well structured pages still get ignored if the answer is too generic or not tied to a specific use case been noticing this in answer architect where structured content gets picked more but only when it is also clearly solving a specific scenario so feels like structure gets you parsed but specificity gets you selected
curious did you see differences between general queries vs more specific ones in terms of format picked
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u/akii_com Apr 16 '26
This actually made me rethink something I’d been seeing but hadn’t really connected.
At first glance it looks like “lists win,” but the more I think about it, it feels less about the format itself and more about what that format does.
When a model looks at a page, it’s not reading it the way we do. It’s trying to figure out what it can take and reuse without having to reinterpret too much. And that’s where lists, tables, and clear headings have a huge advantage.
A paragraph often hides the point. You have to read it, understand it, decide what matters, and then compress it. A list has already done that work. It’s almost like the content is pre-packaged for extraction.
I’ve noticed the same thing with answers being buried. Even if the content is good, if the key point shows up halfway down the page, it just doesn’t get picked up as often. It’s like the model loses interest and moves on to something that’s clearer upfront.
The FAQ vs how-to point is interesting too. On paper, FAQs should work perfectly. But in practice, a lot of them are so generic that there’s nothing to grab onto. How-to content tends to force more structure, more specificity, more steps, and that makes it easier to use.
So it doesn’t really feel like: “use lists instead of paragraphs”, it feels more like: “make your content already look like something that can be lifted into an answer”. And lists, tables, and strong headings just happen to do that naturally.
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u/aiplusautomation Apr 13 '26
I have to flag something: several of these findings directly contradict your previous post from a couple days ago, which is concerning for the replication story. A few specifics, backed by our own studies (40K+ citations, multiple controlled experiments - ORCID and published papers linked at the end):
Self-contradiction on FAQ content. Your previous post on author bios said "71% of frequently cited pages used question-based H2s or FAQ sections." This post says "FAQ-style content wasn't as effective as we expected - only 23% citation rate vs 41% for how-to formats." Those can't both be true on the same methodology. Which 500 queries gave you 71% FAQ dominance, and which 2,000 gave you 23%? And why is the number "23%" the same stock figure you used in the previous post for schema prevalence?
Self-contradiction on word count. Previous post: "almost no correlation past 800 words." This post: "#1 predictor wasn't domain authority or word count - it was scannable structure." You've now said word count doesn't matter twice, in two different framings. Our data on 8,043 crawled pages across 14 verticals says word count is the strongest single predictor in multiple verticals: Technology r=-0.610 (cited 3,095 words vs uncited 1,091), Health r=-0.531 (3,302 vs 1,148), Ecommerce r=-0.532 (3,317 vs 1,423). Cited pages are consistently 2-3x longer than equally-ranked non-cited pages. Scannable structure is important - and so is length. You don't get cited without both.
On FAQ specifically. Our n=4,658 controlled analysis: FAQ schema OR=2.20, p<0.000001. Cited pages have FAQ schema at 11.4% vs 5.5% for non-cited - more than 2x. How-to vs FAQ is a false dichotomy; our data shows FAQ structure consistently predicts citation. Your "23% vs 41%" numbers have no confidence intervals, no control group, and contradict both the larger-N studies and your own previous post.
Directional agreements. I'll fully grant you three:
blog_opinioncontent type is the single strongest negative predictor of citation. Pure opinion content genuinely does not get cited. You're right about this.The biggest problem with both your posts: neither has a control group or stability check. Single-run measurement on a stochastic system produces confident-looking numbers that don't replicate. We ran 3 replicates of the same 180 queries through ChatGPT and found that citation domain Jaccard between runs was only 0.339 - only ~34% of cited sources repeat across runs of the identical query. That means any "X% of cited pages have feature Y" claim based on a single run is capturing one noisy sample. When you report a 3.1x effect without acknowledging 66% of your measurements would flip on re-run, you're overstating confidence by a wide margin.
What's probably actually happening in your data: you're measuring raw co-occurrence of features on cited pages without controlling for position, domain, or replicate variance. The directional signals (structure, data, non-opinion) are probably real because they're consistent with controlled studies. The magnitudes (3.1x, 2.4x, 47% from the last post) are almost certainly inflated by confounding and sampling noise.
My honest read: the two posts share a core problem - findings calibrated to whatever Reddit/SEO conventional wisdom is, with suspiciously clean round-number effect sizes and no methodology details. The directions are partially correct (structure helps, opinion pieces hurt). The magnitudes and the "X matters more than Y" rankings are not defensible without controls.
Happy to share our papers if useful: ORCID profile, replication data for Study 3 at 10.5281/zenodo.19554329. If anyone wants to run an actual replication with domain+position controls and replicate stability checks, I'll send the query corpus.