Short answer. The largest published study of the question found that what moves citation rates is evidence, not repetition. Across 10,000 queries, adding statistics raised citation visibility by up to 40%, authoritative quotations by roughly 30%, and improved fluency by 15–30%, while keyword stuffing scored −10% and keyword density showed minimal influence. Everything practical follows from that: put a number with a source in the passage, quote a named authority, write the answer so it survives being lifted out of the page, and make sure a crawler can read it without running JavaScript. Volume is not on the list.
Most advice about getting cited by AI answer engines is asserted rather than measured. This piece separates the two: what has been benchmarked, what is mechanically necessary, and what is folklore worth ignoring — followed by a page-level rubric you can score your own content against this afternoon.
What the research actually found
Aggarwal et al., "GEO: Generative Engine Optimization" (ACM SIGKDD 2024), benchmarked content modifications across 10,000 queries and measured the change in citation visibility.
| Modification | Measured effect |
|---|---|
| Adding statistics | Up to +40% |
| Adding authoritative quotations | Roughly +30% |
| Improving fluency and clarity | +15% to +30% |
| Adding citations to sources | Positive |
| Keyword density optimisation | Minimal influence |
| Keyword stuffing | −10% |
Two things are worth drawing out.
The winners are all forms of evidence. Statistics, quotations, sources. A model constructing an answer is assembling verifiable-looking claims; a passage that supplies one is more useful to it than a passage that asserts quality.
The losers are all forms of classical keyword optimisation. Not merely neutral — keyword stuffing measured negative. Tactics carried over from a decade of SEO practice are, in this specific measurement, actively harmful.
The study is not the last word — it predates the current generation of models and does not cover every engine — but it is the largest controlled measurement available, and it points consistently in one direction.
What is mechanically necessary before any of that matters
Content quality is irrelevant if the content never reaches the engine. Three checks, in the order they fail:
1. The answer must exist in the served HTML. Load your key page with JavaScript disabled and read what is left. Content that only appears after hydration arrives empty at crawlers that execute no JavaScript. This is the single most common reason a well-written page is invisible, and it is invisible in turn to everyone testing in a browser.
2. AI crawlers must actually be served the full page. Name the relevant user agents explicitly in robots.txt rather than relying on a wildcard, then verify by fetching as each agent and comparing byte counts against a browser fetch. Rate limiters and bot walls that quietly return a shorter page are common, and they cannot be detected from inside the site.
3. Attribution must be unambiguous. Self-consistent canonicals, single-hop redirects, structured data that matches the visible page, and real dates. An engine that cannot decide which URL owns a claim is less likely to attribute it to you.
Watch for content that renders as a placeholder in the served HTML — counters that animate up from zero, figures injected at runtime, content behind tabs. A crawler reading "0+ languages" is reading a stated fact.
What makes a passage liftable
The unit an engine uses is a self-contained span of text, not a page. Six properties, each testable:
- Question as a literal heading, phrased the way buyers ask it — not an internal product name.
- The answer in the first two sentences, with elaboration after. A narrative that arrives at the answer in paragraph nine competes badly against one that opens with it.
- Self-containment. No unresolved "as described above". Read each passage alone and ask whether it still makes sense.
- A definition stated plainly, in one sentence, quotable verbatim.
- Specific and dated claims. "Measured 10 August 2026" beats "recently".
- A formula or a method where one exists. Rarely used, and unusually effective — a passage that defines how something is calculated is far more quotable than one asserting that your quality is high. Most vendor sites in most categories carry no formulas at all in body copy.
A page-level rubric
Score any page out of 20. Anything below 12 will struggle regardless of how much traffic it gets.
| Signal | Points | Pass condition |
|---|---|---|
| Statistics per 1,000 words | 4 | ≥ 8, each with a source |
| Named authoritative quotations or references | 3 | ≥ 2, resolvable |
| Question-shaped headings | 3 | Every major section |
| Answer-first structure | 2 | Direct answer within two sentences of each heading |
| Self-contained passages | 2 | No cross-references needed to understand a section |
| Plain definitions | 2 | At least one one-sentence, quotable definition |
| Formula or defined method | 1 | Present where the subject allows |
| Correct, non-contradictory schema | 1 | Matches the visible page |
| Real publication and modification dates | 1 | Reflect actual changes |
| Readable with JavaScript disabled | 1 | Full text present in served HTML |
Evidence density = Statistics with a source ÷ (Word count ÷ 1000)
Eight per thousand words is a workable target. It sounds high until you count what already qualifies: dates, counts, thresholds, versions, durations, percentages — provided each has a source or is directly verifiable.
What does not work, and why it persists
Publishing volume. Sixty thin articles a quarter adds crawl cost and little else. The measured lever is evidence density, not surface area. Volume persists as advice because it is easy to sell and easy to deliver.
Keyword density targets. Measured as minimal influence, and stuffing measured negative. Carried over from a practice where it did once matter.
A special file or schema type that grants inclusion. There is no "AI Overviews schema". llms.txt may help some AI systems and is not used by Google Search; treat it as a low-cost addition after the fundamentals, never as a route into a specific engine's answers.
Guaranteed placement. Nobody controls the output of a model they do not operate, and answers vary between runs.
Optimising for one engine. The content work is largely shared across engines; what differs is measurement and time-to-effect. Building separately per engine duplicates the expensive part and skips the cheap part.
How to know whether any of it worked
Measure before you change anything, or nothing afterwards is attributable.
- A fixed prompt set — 20 to 40 questions per market, held constant, covering category, comparison and brand questions.
- Both surfaces, reported separately. A model answering from its training weights moves on model-release timescales; the same model with browsing enabled moves in weeks. Blended, a genuine retrieval win stays hidden for months.
- Multiple runs per prompt, because generative answers vary between runs.
- Raw answers retained. The number says something moved; the text says why.
Share of answer = Answers mentioning the brand ÷ Total answers for the prompt set
Cited share = Answers linking or attributing the brand ÷ Answers mentioning the brand
Expect the retrieval surface to move first. If it does not, check delivery before rewriting content — most "the content didn't work" outcomes are pages an engine never received in full.
How Lifewood approaches this
Lifewood runs this as a measured programme rather than a set of recommendations, on client sites and on its own. The instrument is in-house: fixed prompt sets, a pre-work baseline, memory and retrieval reported separately, raw run files retained and readable.
The rubric above is applied as a pre-ship gate rather than an audit performed afterwards — a page that fails on evidence density or self-containment is rewritten before publication, because retrofitting evidence into finished copy is far more expensive than writing it in. For multi-market programmes the constraint is authorship: 50+ languages, 40+ delivery centres across 30+ countries and 56,788 contributors mean content written in-market rather than translated, which matters because the question a buyer asks in Vietnamese is rarely the English question in Vietnamese.
See AEO services, GEO services, and GEO vs AEO vs traditional SEO for how the three disciplines divide.
Sources and further reading
- Aggarwal et al., "GEO: Generative Engine Optimization", ACM SIGKDD 2024 — the 10,000-query benchmark behind every figure in the table above.
- Companion guide: How to Improve ChatGPT Brand Visibility in 2026 — the seven-step execution sequence this rubric sits inside.