- The strongest documented lever, per the actual Princeton/Georgia Tech GEO research, is adding statistics, citations, and quotations. But that's not a formatting trick. It works because it adds real, checkable information a model can point to, not because of stat density.
- Extractable content answers directly, near the top of a section, without requiring the reader (or the model) to stitch context together from elsewhere on the page.
- Original data beats a paraphrase of someone else's stat: a model has no reason to cite the tenth restatement of the same figure over wherever it actually originated.
- Self-contained sections matter more than sentence length. A longer sentence stating one complete, specific claim beats three short, vague ones that depend on each other for meaning.
- Packaging is a separate lever from claim specificity: tables, comparison blocks, and FAQ-formatted Q&A pairs are easier for a model to lift as coherent, self-contained retrieval units than the same information embedded in flowing prose.
- This is the craft layer of Stage 3: see the GEO paper explained for the actual research this is built on, in more depth than a one-line summary.
The single best-documented lever for this, from the actual Princeton/Georgia Tech research (covered in full in the GEO paper explained), is adding statistics, citations, and quotations to content. "Add more stats" describes a result, not a technique on its own. The craft is in how you do it, which is what the patterns below actually show, with real rewrites rather than the principle restated again.
Pattern 1: should you lead with the direct answer, not the setup?
Before: "There are a lot of factors that go into how AI search engines decide what to cite, and it's a topic that's gotten a lot of attention lately as more brands try to understand how to improve their visibility in this space."
After: "AI search engines cite sources based on three factors: content extractability, entity resolution, and third-party validation, in that order of what breaks first when it's missing."
The first version spends 40 words establishing that the topic exists and matters before saying anything. The second states the actual answer in the first sentence. A model extracting a quotable answer from the first version has to read past a full paragraph of throat-clearing to find anything with content in it; the second gives it something usable immediately.
Pattern 2: does original data beat a recycled stat?
Before: "Studies show that AI-referred traffic converts significantly better than traditional search traffic."
After: "Microsoft Clarity's study of 1,200+ publisher sites found LLM referral traffic converting at 1.66% for sign-ups, against 0.15% for search, with Copilot referrals specifically converting at 17x the direct-traffic rate."
The first sentence is a claim with no attribution and no number a reader (or a model) could verify or requote. The second names the source, the sample, and the exact figures: it's the kind of sentence a model can lift directly into a generated answer and still be accurate, because the claim carries its own verification with it.
Pattern 3: should sections be self-contained, not context-dependent?
Before, split across two paragraphs: "There are several stages to this process. The first one is about making sure the technical foundation is solid.
This matters because without it, nothing else in the sequence works, since the rest depends on this being handled first."
After, self-contained: "Stage 1 confirms AI crawlers can actually reach your site: checking robots.txt, server logs, and CDN/WAF configuration. Nothing downstream produces citations if this stage fails, because a crawler that can't reach you can't cite you."
The "before" version requires reading both paragraphs, and even then "this" and "the rest" are vague pronouns depending on context established elsewhere on the page. The "after" version names the actual stage, states what it means, and states the consequence, extractable as a standalone unit even if a model only pulls this one paragraph out of the full page.
Pattern 4: does a specific quotation beat a paraphrase?
Before: "Experts generally agree that entity consistency is important for AI visibility."
After: ""A model doesn't cite a name: it cites an entity it has some confidence it has correctly resolved," as covered in what is entity consistency."
"Experts generally agree" attributes a claim to nobody in particular, which gives a model nothing to actually cite: there's no source, no name, no specific claim being made by anyone identifiable. A direct quotation, attributed and linkable, is something a model can accurately attribute in a generated answer instead of having to either drop the claim or invent an attribution for it.
Pattern 5: should content be structured as a retrievable block, not just clean prose?
Before: A comparison between three options written as continuous prose: three paragraphs, each describing one option's price, features, and tradeoffs in sentence form, with the actual differences scattered across the paragraph rather than aligned against each other.
After: The same comparison as a table: one row per option, one column per attribute (price, key feature, best-fit use case), with a single sentence above it stating what's being compared and why.
This is a different lever from the four above, about packaging rather than word choice: making the content extractable as a self-contained unit. A generative engine assembling an answer favors content it can lift as a coherent block (a table, a numbered list, a definition, an FAQ pair) over the same information buried inside flowing paragraphs it has to parse and restructure itself first. Tables, comparison blocks, and FAQ-formatted Q&A pairs are the concrete formats worth reaching for whenever the content is inherently comparative or enumerable; prose still does the job better for content that's inherently narrative or causal, where forcing it into a table would strip out the actual reasoning.
What doesn't work?
The GEO paper tested keyword stuffing as one of its nine methods, alongside the ones that actually moved the needle, and it was not among the top performers, consistent with the general pattern across the whole study that methods adding genuine information (statistics, citations, quotes) outperformed stylistic or volume-based tactics. Writing more words, or repeating a target phrase more often, isn't the lever. The lever is a higher density of specific, original, checkable claims per section, which usually means writing less throat-clearing, not more content overall.
Frequently asked questions
What makes content "extractable" to an AI model?
A direct, self-contained answer that doesn't require the model to stitch together context from three other paragraphs, original data or a specific claim rather than a paraphrase of something already said elsewhere, and a structure (a clear heading, a direct opening sentence) that signals exactly what a section answers before the model has to infer it.
Does adding more statistics automatically make content more citable?
Not any statistic: a specific, original, or clearly-sourced one. The Princeton/Georgia Tech GEO paper's own finding is domain-dependent, and the effect comes from the statistic adding real, checkable information the model can point to, not from stat-density as a formatting trick. A vague, unsourced number is close to worthless for this purpose.
Should I write shorter sentences for AI search?
Sentence length isn't the lever: clarity and self-containment are. A longer sentence that states a complete, specific claim is more extractable than three short, vague ones that each require the others for context. Simplicity/readability was one of the methods the GEO paper tested and found to help, but it wasn't about short sentences specifically: it was about the content being genuinely easy to parse and unambiguous.
Is this the same as writing for a Google featured snippet?
Related, but not identical. A featured snippet rewards one crisp, extractable answer near the top of a page (good practice here too). But a generative engine is synthesizing an answer from potentially several sources at once, not lifting one block verbatim, so it also rewards claims specific and well-sourced enough to survive being paraphrased and recombined with other sources, which a snippet doesn't require. The full mechanical comparison is in AI Overviews vs. featured snippets.
Sources referenced
- Princeton/Georgia Tech GEO paper (arXiv 2311.09735)
- Microsoft Clarity: AI traffic converts at 3x the rate of other channels (2026, 1,200+ publisher/news sites)
This piece is the craft layer of Stage 3 in the five stages for winning in the AI search era. For the actual research these patterns are built on, see the GEO paper explained.
