Generative Engine Optimisation (GEO)

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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.

How GEO differs from SEO

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.

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.
  • 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.

What GEO content looks 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.

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.

How to 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. Track citation alongside rankings. Use the AI Overview filter in Search Console (rolled out September 2025), and monitor mentions across Perplexity, ChatGPT, and other surfaces manually for a representative sample of queries.

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

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.