Generative Engine Optimisation (GEO)

Generative Engine Optimisation (GEO) is the practice of structuring and positioning content to be retrieved, parsed, and cited by AI-powered answer engines. It overlaps with traditional SEO heavily, but the goals diverge in places: GEO optimises for citation, not click-through.

Google’s stance: GEO is SEO

Google’s May 2026 AI optimisation guide explicitly confirms:¹ “Optimising for generative AI search is optimising for the search experience, and thus still SEO.” From Google’s perspective, there is no separate GEO discipline. Sites that perform well in traditional search, through quality content, clear structure, and authoritative authorship, perform well in Google’s AI-generated answers without special optimisation. The guide also directly addresses tactics sometimes promoted as essential for GEO: llms.txt files, content chunking, AI-specific markup, and AI-targeted rewrites, classifying them as unnecessary engineering for Google’s systems. Other AI search platforms (Bing, Perplexity, ChatGPT Search) have not issued equivalent guidance and may weight signals differently.

How does citation-focused optimisation differ from ranking-focused optimisation?

Traditional SEO optimises a page to rank in a list of results. The user clicks the result, lands on the page, reads the content, and possibly converts. The page is the destination.

GEO optimises a page to be retrieved by a generative system, which then synthesises an answer from one or more sources and presents that answer (with citations) inside its own interface. The page is no longer the destination, it is a source for synthesised answers.

The implications:

• Click-through rate matters less; citation rate matters more. A page cited in AI Overviews 10,000 times that earns 200 clicks is performing differently than one ranked #3 organically with the same impression volume. Both have value, but the value is different in kind.² The compensating factor: LLM-referred visitors convert at twice the rate of other traffic sources, with a third fewer sessions.³
• Brand visibility extends beyond the click. When a page is cited, the brand is named in the response even if the user never visits. That visibility has compounding effects on entity recognition.
• Passages, not pages, are the unit of retrieval. AI engines extract specific passages that answer specific questions. A 4,000-word article rarely gets cited as a whole; one paragraph from it might. Microsoft’s Web IQ, a Bing-based grounding API launched in June 2026, evaluates passages against GDSAT (completeness, freshness, authority) independently of the page they sit on.⁴ Microsoft says Web IQ grounds Copilot directly and supplies ChatGPT with “some of its web answers”,⁵ so passage-level selection governs a substantial share of what those two surfaces cite.

What does the evidence actually say works?

Most GEO advice is inference from how retrieval systems appear to behave. One body of evidence is not, and it is worth separating out, because it is the closest thing the discipline has to a controlled result.

The paper that coined the term tested content edits against generative engines and measured the change in visibility.⁶ Three of its interventions stand out, and they are the same three:

• Add statistics. Replacing vague quantities with specific figures.
• Add quotations. Quoting named, credible sources directly.
• Add citations. Attributing claims to their sources.

The strongest methods improved on the baseline by 41% and 28% on the study’s two visibility measures, with reported gains of up to 40% overall.⁶ The paper also found the effect is not uniform: statistics helped most in domains like law and government and on opinion-shaped questions, while quotation addition did most work in explanatory and historical content.⁶

Two caveats keep this honest. The study predates the current generation of AI search products, so treat it as directional evidence about how generative systems weigh evidential content rather than a live specification of any one engine. And its findings converge with what Google says anyway: content that states things precisely and attributes them properly is simply better content. That convergence is the point. It is the rare GEO tactic that is both evidenced and free of downside, which is why it belongs at the top of the list rather than buried under llms.txt debates.

GEO, AEO, LLMO: why there are so many names

The practice described on this page goes by several names. GEO is used here because it has the clearest academic provenance: it was coined in a 2023 research paper by Aggarwal and colleagues examining how content performs in generative answer engines.⁶ The others are worth knowing.

AEO (Answer Engine Optimisation) is the oldest term. It predates generative AI, originally describing optimisation for voice assistants and featured snippets: surfaces that return a direct answer rather than a list of links. Since large language models became prominent in search, AEO has been used more broadly and often interchangeably with GEO. Some practitioners draw a narrower distinction: AEO for Google’s own AI features (AI Overviews, AI Mode), GEO for third-party systems such as Perplexity and ChatGPT. Neither usage is wrong; the overlap is near-total.

LLMO (Large Language Model Optimisation) emphasises the model layer: influencing what LLMs incorporate into their training data, beyond what they retrieve in real time. In practice, the content tactics for LLMO are indistinguishable from GEO. The framing differs; the work does not.

AIO appears in two contexts: as shorthand for AI Overviews (the Google feature), and as a generic label for any AI-related optimisation. The ambiguity makes it imprecise as a standalone term.

AI SEO is a catch-all. It covers the application of SEO thinking to AI search surfaces, which is precisely what GEO and AEO already describe.

None of these represent separate disciplines with separate playbooks. The content signals that earn citations are consistent across all of them: accuracy, clear structure, direct answers, credible authorship, cited sources. The label matters less than understanding why AI retrieval systems favour certain content patterns.

What does GEO content look like?

The patterns that correlate with citation across multiple AI surfaces (AI Overviews, Perplexity, ChatGPT Search) are consistent.

Direct, definitional opening sentences. The first sentence of a section should answer the question that section addresses. Burying the answer in a third paragraph reduces the probability that a retrieval system extracts it cleanly.

Question-shaped headings. H2s and H3s phrased as questions match conversational queries closely. “What is X?” outperforms “Understanding X” for retrieval purposes, even though they cover the same material.

Stand-alone passages. Each section should make sense extracted on its own. Heavy use of pronouns referring back to earlier sections (“As we saw above…”) makes a passage harder to use as a citation.

Explicit attribution and citation. AI systems favour content that itself cites primary sources. Linking out to original research, official documentation, and named experts is a trust signal.

Structured data formats. Tables, lists, definition pairs, and step-by-step instructions are easier to parse than dense prose. They also map cleanly onto the formats AI engines tend to render in their answers.

Named author with verifiable credentials. Anonymous content from a faceless brand competes from a weaker position than content with a named expert author whose authority can be cross-referenced.

Neutral framing over self-promotion. AI answers reward content that reads as an even-handed authority, not a sales page. A June 2026 analysis of 100 B2B “best software” queries by Lily Ray found Google’s AI Overviews frequently cited a brand’s own “best [category]” listicle as a source while leaving that brand out of the recommendation, around 69% of the time, recommending competitors or Reddit, Forbes, and YouTube instead.⁷ Being cited as a source and being the recommended answer are different outcomes; self-serving framing earns the first and loses the second.

Is GEO only about on-page content?

No, and treating it that way is the most common mistake in the discipline. Everything above concerns the page itself: its structure, its passages, its attribution. That work determines whether a page can be cited cleanly once a retrieval system reaches it. It does not determine whether the brand is a candidate in the first place.

Ahrefs’ December 2025 analysis of 75,000 brands found the strongest off-site correlates of AI visibility were not links but mentions: branded web mentions correlated at 0.664 on ChatGPT, 0.709 on AI Mode and 0.656 on AI Overviews, against roughly 0.19 to 0.25 for backlinks.⁸ The authors are careful to note that correlation is not causation, and the confound is obvious enough that it should temper any strong claim: brands that get written about are usually brands that are already established, and establishment drives both variables. An earlier Ahrefs study found the same correlation was strong for AI Overviews but weak for Perplexity and ChatGPT, so the effect is unlikely to be uniform across platforms.⁹

Set the correlation aside and a structural argument remains. Each engine draws from its own source pool, and those pools diverge: Semrush found Google’s AI Mode shared only around 54% of its cited domains with Google’s own organic top 10, and ChatGPT’s citations tracked Bing more closely than Google.¹⁰ A page you own, however well structured, sits in one index. Coverage across third-party publications reaches source pools your domain cannot, which is why digital PR and brand mentions sit inside a GEO programme and not beside it. The on-page work makes a page citable; the off-page work makes the brand a candidate for citation.

What GEO is not

GEO is not “writing for robots” in the keyword-stuffing sense. AI retrieval models are evaluating the same quality signals that Google’s quality raters look for, just programmatically. Pages that read as machine-targeted (repetitive phrasing, keyword density above natural language norms, content padding) are penalised by both human readers and the systems learning from them.

GEO is also not a wholesale replacement for traditional SEO. The two share roughly 80% of their underlying signals. A site optimised well for traditional SEO is already most of the way to being well-optimised for AI retrieval. The remaining 20% is where the discipline lives.

GEO is also not a strategy for social discovery. Social search on platforms like YouTube, Reddit, and Instagram has distinct signals from traditional search. YouTube SEO, Reddit SEO, and Instagram search each require platform-specific approaches, covered under search everywhere optimisation.

Tactics Google explicitly debunks

Google’s May 2026 guide directly addresses ineffective tactics sometimes promoted as essential for GEO:

llms.txt files. Creating special machine-readable files is unnecessary for Google’s AI systems, which parse web pages directly. Do not publish expecting SEO or GEO benefits in Google. Anthropic publishes its own llms.txt for documentation purposes and co-developed the llms-full.txt format with Mintlify; whether Claude reads other sites’ llms.txt at inference time is unconfirmed.¹¹

Content chunking. Artificially breaking paragraphs into bullet points or short sentences to trigger AI retrieval does not work. It signals poor editorial quality and reads unnaturally. AI systems favour naturally-written content.

Schema stuffing. Adding FAQ schema to pages without genuine FAQs, or marking up content as HowTo or Article when unwarranted, adds noise. Schema reinforces what good content already communicates; it does not substitute for quality.

AI-specific rewrites. Rewriting existing content specifically for AI systems is ineffective. Content rewritten to sound “for robots” underperforms versus naturally-written content.

Over-structuring content. Breaking every paragraph into lists or forcing every heading into a question produces poor readability and signals low editorial quality. Structure should serve human readers first.

Volume without substance. Publishing large quantities of AI-generated content hoping to capture more citation surface tends to backfire. AI systems favour accuracy, clear authorship, and genuine expertise. Scale without human review optimises against the signals that earn citations.

The common thread: tactics treating GEO as a separate game to be won independently of content quality will underperform. The sites earning consistent AI citations are those that would have ranked well in traditional search anyway. Understanding how AI search works, particularly the citation quality filters applied before a passage is included in an answer, explains why AI content risks, including hallucination and E-E-A-T erosion, result in citation exclusion rather than just ranking drops.

How do you start with GEO?

1. Audit existing content for retrievability. For your top 20 pages, check whether each major section opens with a clear answer to a specific question. If not, rewrite the opening.
2. Reshape headings into question form where the section content answers a question. Keep the rewrites natural; don’t force every heading into a question if it isn’t one.
3. Add or strengthen schema markup. Article, FAQ, HowTo, and Product schema are the most useful types for AI retrieval signals.
4. Strengthen author attribution. Visible bylines, author bio boxes, Person schema with sameAs links to LinkedIn and other authoritative profiles.
5. Build off-page coverage deliberately. Audit where the brand is mentioned across publications the engines actually cite, then close the gaps through digital PR, original research, and expert commentary. This is the slowest of the five steps and the one most often skipped, because it sits outside the SEO team’s direct control.
6. Track citation alongside rankings. In GSC, monitor CTR trends against stable impressions in the Performance report (Web search type): a falling CTR is the primary signal that an AI Overview is absorbing clicks. There is no dedicated AI Overview filter. Monitor AI-driven traffic in GA4’s AI Assistant channel, which captures ChatGPT, Claude, Gemini and Perplexity automatically.⁶ Supplement with manual sampling across Perplexity, ChatGPT, and other surfaces for citation tracking where referrer data is incomplete.

The GEO question for any piece of content

When deciding whether to publish, expand, or rewrite a page, ask: would a retrieval system extracting a passage from this page produce something a user would find useful and trustworthy? If yes, the page is GEO-ready. If the best passage requires several paragraphs of context to make sense, restructure until a stand-alone passage exists.

Frequently asked questions

What is the difference between GEO, AEO, LLMO, and AI SEO?
They largely describe the same practice under different names. AEO (Answer Engine Optimisation) is the oldest, originating in the voice search and featured snippet era. GEO (Generative Engine Optimisation) is more precise, coined in academic research to describe optimisation for systems that synthesise answers from retrieved content. LLMO (Large Language Model Optimisation) emphasises influencing LLM knowledge rather than real-time retrieval; in practice the tactics are nearly identical to GEO. AIO and AI SEO are broader terms with no distinct practice behind them. The content signals that earn citations are consistent across all these surfaces.

Is GEO a real discipline or just SEO rebranded?
Both. The underlying signals are mostly shared with traditional SEO. The framing, measurement, and content patterns are different enough to warrant separate language. Treat GEO as the part of SEO concerned with retrieval and citation in generative systems, not as a replacement.

Can I optimise for one AI engine without affecting others?
Generally no. The retrieval and quality signals across major AI search surfaces are highly correlated. Content optimised for AI Overviews tends to perform similarly in Perplexity and ChatGPT Search, with minor variations.

Does GEO require sacrificing traditional SEO performance?
Rarely. Most GEO improvements (clearer structure, direct answers, better schema, stronger attribution) also help traditional rankings. Cases where the two diverge meaningfully are uncommon.

Footnotes

1. Google’s AI optimization guide ↩

2. AI Overviews Reduce Clicks by 58% — Ahrefs ↩

3. 2026 AEO/GEO Benchmarks Report — Conductor ↩

4. Announcing Microsoft Web IQ — Bing Search Blog ↩

5. Microsoft Web IQ (Microsoft names “AI platforms like Copilot and OpenAI”), with Jordi Ribas, President of Search and AI, quoted in Microsoft releases Web IQ, powered by Bing but designed for how AI-agents search — Search Engine Land ↩

6. Google Analytics AI Assistant traffic measurement roll-out — Google Analytics LinkedIn ↩ ↩² ↩³ ↩⁴ ↩⁵

7. Google AI Overviews cite self-serving listicles, but recommend competitors 69% of the time — Search Engine Land ↩

8. Top Brand Visibility Factors in ChatGPT, AI Mode, and AI Overviews (75k Brands Studied) — Ahrefs. Spearman correlations, December 2025. The authors explicitly caution that correlation is not causation. ↩

9. Google Seems More Biased Towards Big Brands Than ChatGPT and Perplexity — Ahrefs. Based on ~76.7M AI Overviews and roughly 950,000 prompts each for ChatGPT and Perplexity, June 2025. ↩

10. How Google’s AI Mode Compares to Traditional Search and Other LLMs — Semrush. 5,000 keywords, 150,000+ unique citations, July 2025. ↩

11. Mintlify: The value of llms.txt ↩