Generative Engine Optimization (GEO)

GEO is the set of practices aimed at increasing the likelihood that a generative AI system selects a piece of content as a source when composing an answer. It sits alongside AI search visibility as the broader outcome GEO is trying to produce, and alongside Answer Engine Optimization (AEO), a closely related term with heavy overlap and no formal boundary between the two (see the AEO entry for how the terms differ in practice).

The original GEO research tested how different content optimisations affected a source’s visibility inside AI-generated answers, and found that specific formatting and content choices moved the needle more than others. The exact figures are specific to that study’s test set and shouldn’t be read as universal guarantees, but the direction of the finding, that content can be deliberately shaped to be more citable, holds up as the basis for the discipline.

• Goal: SEO aims for a ranking position. GEO aims for inclusion inside a generated answer, with or without a visible ranking.
• Signals: SEO weighs backlinks, keyword relevance and technical crawlability heavily. GEO weighs quotability, specificity and cross-source corroboration more heavily.
• Measurement: SEO has mature tooling (Search Console, rank trackers). GEO tooling is younger and less standardised; see AI citation tracking.
• Timeframe: A page’s SEO ranking is relatively stable day to day. A model’s citation choices can shift between two runs of the same prompt, since generation is probabilistic.

Google’s own position is worth stating plainly here, because it cuts against a lot of GEO marketing. Its Search Central guidance on optimising for generative AI features says the best practices for SEO continue to be relevant, because Google’s generative AI features are rooted in its core Search ranking and quality systems, and that optimising for generative AI search is optimising for the search experience, and thus still SEO. It states that structured data isn’t required for generative AI features, and names several things site owners do not need to do: publish an llms.txt file, chunk content into tiny pieces, or rewrite content specifically for AI systems. On Google’s surfaces at least, GEO is not a separate technical discipline.

That guidance covers Google. It says nothing about ChatGPT, Perplexity or Claude, which retrieve differently and publish no equivalent guidance, so the distribution question below still stands.

In practice, most teams treat GEO as an addition to their SEO programme, not a replacement for it. The underlying content asset is often the same; what changes is how it’s written and structured, and where else it needs to exist beyond the company’s own site.

1. 1.Write direct, quotable statements. A sentence that states a claim plainly is easier for a model to lift and cite than a paragraph that builds to a point.
2. 2.Back claims with sourced statistics. Numbers tied to a named source (a study, a benchmark, an internal figure) read as more citable than unattributed claims.
3. 3.Structure content for extraction. Clear headings, short paragraphs and direct-answer formatting near the top of a section help both search engines and AI retrieval systems parse the content.
4. 4.Publish across more than one domain. A claim that appears, consistently, on a company’s site, on LinkedIn, and in independent coverage is more likely to be treated as corroborated than the same claim published once. Ahrefs’ study of 75,000 brands found branded web mentions among the strongest correlates of AI visibility (Spearman coefficients of 0.664 in ChatGPT, 0.709 in AI Mode and 0.656 in AI Overviews), well ahead of backlink counts, which sat in the 0.19 to 0.3 range. Ahrefs are explicit that correlation is not causation, so read this as a pattern worth noting rather than a lever guaranteed to work.
5. 5.Attribute expertise to named people. Content tied to a real person with visible expertise tends to be treated as a stronger source than unattributed brand copy.

LinkedIn has published its own guidance on being cited by AI answer engines, and it lines up closely with the tactics above. Its headline points: educational content is the most valuable, fresh and original posts perform best, and author credibility counts for more than viral reach.

The specifics worth noting:

• Originality is decisive. The 95% figure LinkedIn leads with, that 95% of all citations of LinkedIn content come from original posts rather than reshares, is Semrush research, credited as such in LinkedIn’s own guidance. Semrush arrived at it by analysing 89,000 LinkedIn URLs cited across 325,000 prompts run through ChatGPT Search, Google AI Mode and Perplexity in January and February 2026. In the same study, articles accounted for 50 to 66% of cited LinkedIn content and feed posts for 15 to 28%, depending on the engine. LinkedIn’s own internal data, alongside a Profound report, puts long-form articles, newsletters and posts together at around 60% of all citations.
• Lead with the answer. A question then answer structure, with the most important information in the opening line or title, helps both people and models grasp relevance immediately.
• Author credibility counts, but less than reach-chasing would suggest. LinkedIn’s internal data points to a threshold around 3,000 followers, above which members show a stronger likelihood of being cited. Semrush’s figure is lower: just under 50% of cited post creators had 2,000 or more followers, and roughly 75% of cited authors had published five or more posts in the preceding four weeks. Consistency of publishing shows up at least as strongly as audience size, which is why a mix of company-page posts and named subject-matter experts works better than the brand account alone.
• Practical formatting. Ranked lists and clear steps, specific dates that signal freshness, and captions on video (so models have text to read) all help.

Henrik Ihlo, Director of LinkedIn Marketing Solutions DACH, frames the same shift in strategic terms: AI systems reward educational, answer-first, original and well-structured content, and thought leadership from companies and their leaders is becoming the infrastructure that shapes how a brand is described and ranked by AI. His practical advice mirrors LinkedIn’s data. Teach rather than broadcast, answer real questions directly, bring an original perspective backed by your own data, and keep structure clean and consistent.

GEO is measured through AI citation tracking: running a defined set of prompts across target AI tools on a schedule and logging which sources get cited, in what form, and how that changes over time. Share of model, the percentage of citations a brand earns relative to competitors across that prompt set, is the closest GEO equivalent to a share-of-voice metric.

Because AI models update their retrieval behaviour and underlying weights over time, GEO measurement needs to be ongoing rather than a one-off audit.