Generative Engine Optimisation (GEO): Getting Cited by AI

What Generative Engine Optimisation Actually Optimises For

GEO is the generation-layer sibling of Answer Engine Optimisation (AEO). AEO gets you into featured snippets and Google AI Overviews. GEO gets you cited when ChatGPT, Claude, Gemini, or Perplexity write a paragraph answering someone's question. The two overlap heavily in technique, but they're solving different problems: AEO extracts an answer from an index it already has, GEO retrieves candidates and generates a new sentence around the ones it trusts. If you want the full AEO vs SEO comparison, that lives in the AEO guide; this one goes deep on the mechanism that's specific to GEO.

How Retrieval-Augmented Generation Decides Who Gets Cited

That grounding step is the entire game. An LLM doesn't "know" your business the way it knows facts baked into its training data; for anything current or specific, it retrieves live candidates, the same way a search engine would, then writes around the ones it decides to trust. Getting into that shortlist of 2-7 trusted sources is what every tactic in this guide serves.

Why the Same Page Performs Differently Across ChatGPT, Claude, Gemini, and Perplexity

The four major engines don't retrieve the same way, so a page optimised for one doesn't automatically perform in another. Perplexity is built as a citation-first answer engine: nearly every response carries inline sources you can click through to. ChatGPT's default answers draw on training data unless a live search is triggered, in which case OAI-SearchBot's index takes over. Claude's web search grounds answers against live retrieval only when that feature is switched on. Gemini leans heavily on Google's own search index, which is one reason the technical SEO fundamentals that feed Google Search also feed Gemini's grounding.

The practical implication: don't assume one citation win transfers to every platform. Test your core buyer questions across all four periodically rather than optimising for whichever one you happen to check.

Share of Model: GEO's Core Metric

Traditional SEO tracks share of voice: what percentage of search impressions you captured. GEO's equivalent is Share of Model: of all the times someone asks an AI assistant a question in your category, what percentage of answers cite you. If 100 people ask ChatGPT "who provides AI training for businesses in New Zealand" and you're cited 40 times, that's a 40% Share of Model for that query. It's the visibility metric GEO work is actually trying to move.

Maximise Information Gain

Information Gain is a concept from a 2022 Google patent that has become a core operational metric for AI citation systems. It refers to unique, valuable information your content provides beyond what competitors already cover. Proprietary data, original research, and direct expert quotes score high. Generic summaries of widely available information score near zero.

LLMs are trained on the consensus of the web. Content that simply echoes what everyone else says adds no new signal; the model already "knows" it. This is why AI-generated content that just paraphrases existing sources has near-zero information gain by definition, and why it's systematically deprioritised by retrieval and grounding systems. A Princeton study on generative engine optimisation found that adding original statistics improved citation rates by 41%, and adding expert quotations improved them by 40%, the two largest single-tactic gains measured.

Practical steps: add at least one original statistic or direct attribution per key page, publish proprietary data from your own work (client results, internal surveys, real project outcomes), and use direct quotes with named attribution rather than paraphrasing.

Entity Clarity: How a Model Confirms You're Real

An LLM doesn't read your About page and take your word for it. Before it grounds an answer on your content, it needs to resolve who you actually are: is the business named on this page the same one that LinkedIn, an industry registry, and (if you have one) Wikidata all know about? That resolution happens through structured identity signals, not marketing copy.

The mechanism is your Organisation schema's @id and sameAs array. @id is a stable, self-referencing URI (typically https://yoursite.com/#organization) that anchors every piece of schema across your site to a single entity, rather than leaving each page's markup floating unconnected. sameAs is the list of external URLs, your LinkedIn company page, industry registry entry, Crunchbase profile, Wikidata item, that all point back to the same identity. Organisation and Person schema together are associated with roughly a 20% increase in AI Overview visibility, on top of the general schema lift covered above. For New Zealand businesses, the sameAs list should also carry your NZBN as a taxID or identifier field, since it's one of the few trust anchors an AI system can verify against a public registry rather than taking your word for it.

Wikidata: The Verification Layer Most Businesses Skip

A Wikidata item, where your business qualifies for one, is one of the strongest sameAs entries available, because it's an independently maintained, third-party-sourced record of who you are rather than something you wrote about yourself. Wikidata's bar for inclusion is lower than Wikipedia's: you generally qualify if you have a Wikipedia article in any language, a mention in a verifiable third-party published source, an official registry identifier, or a reference from another existing Wikidata item. Your own website doesn't count as a source; Wikidata items need external citations to survive.

The link only works in both directions. Your site's sameAs array needs to include the Wikidata QID, and the Wikidata item itself needs a P856 (official website) statement pointing back to your domain. Most businesses that get a Wikidata item stop after the first half and never check that the reverse link exists, which leaves the signal half-built. If your business doesn't yet meet Wikidata's notability bar, don't fabricate coverage to get one created; an uncited, thin item is fragile and at real risk of deletion, which does more harm than not having one at all.

Write Long-Form, Original Content

Length and depth directly correlate with higher citation counts specifically inside conversational answers. Short articles under 800 words average 3.2 citations from ChatGPT, while long-form pieces over 2,900 words earn 5.1. The correlation isn't with word count itself; it's that longer pieces tend to carry more original data, more expert quotes, and more sub-questions answered, all of which independently drive Information Gain.

Create an llms.txt File

An llms.txt file sits at the root of your website and gives AI crawlers a machine-readable summary of your site's purpose, key pages, and core information. It's conceptually similar to robots.txt but designed for LLM agents rather than traditional crawlers. We've implemented one for this site: view our llms.txt as a live example.

Adoption is still early and most AI crawlers don't yet check it as a primary signal. But it costs nothing to create, it's forward-compatible with where AI agent tooling is heading, and it gives you explicit control over how AI systems summarise your business when generating responses. This one is purely a GEO tactic; it has no AEO equivalent, since Google's own crawler and index already have their own summary of your site.

Allow the Retrieval Crawlers

GEO cares most about the crawlers that feed an engine's live retrieval and training: GPTBot and ClaudeBot for training data, PerplexityBot for real-time retrieval. That's a slightly different emphasis from AEO, which leans more on OAI-SearchBot and Google-Extended for search-facing surfaces. In practice you want all five allowed; the AEO guide has the full robots.txt block to copy.

Don't Ignore Where Humans Already Talk About You

Generative engines increasingly treat community platforms as grounding sources in their own right, not just as backlinks. A Reddit thread where someone recommends your business, or a LinkedIn post discussing your work, can end up cited directly inside an AI-generated answer. This is a channel most GEO advice ignores, because it isn't something you control the way you control your own website copy, but it's worth deliberately encouraging: respond to relevant threads under your own name, keep a LinkedIn presence active, and treat every genuine mention as inventory a model might eventually retrieve from.

GEO Within Google's Rules, and Everyone Else's

The AEO guide covers Google's August 2026 spam policy, which explicitly targets content engineered purely to manipulate generative AI responses. GEO doesn't have an equivalent named policy yet, but the same test applies: would you publish the page if no AI system ever cited it? Content built only to be lifted into an answer, with nothing genuinely useful behind it, is exactly the near-zero Information Gain pattern that grounding systems are already built to ignore. Structuring for extractability and manipulating for citation look similar on the surface; only one of them actually works.

Common GEO Mistakes to Avoid

Treating AEO and GEO as the Same Discipline

This is the single most common confusion in current AEO/GEO discussion. AEO is extraction from an index Google already has. GEO is retrieval and generation by a model deciding what to trust in the moment. Techniques overlap; the mechanisms don't. Treating them as interchangeable means you'll optimise for one and assume you've covered both.

Assuming One Engine's Behaviour Predicts Another's

A citation in Perplexity doesn't mean you'll be cited in Claude's grounded search or Gemini's answers. Each engine retrieves differently. Test across all four rather than optimising for whichever one happens to be easiest to check.

Skipping llms.txt Because Adoption Is Early

It's a five-minute file that costs nothing and is forward-compatible with where AI agent tooling is heading. "Not everyone checks it yet" isn't a reason to skip something with zero downside.

Adding Schema Without Anchoring It

Individual pages of Organisation or Person schema with no shared @id are disconnected islands rather than one resolved entity. And a sameAs link that's stale, broken, or points at the wrong company with a similar name actively misleads entity resolution rather than helping it. Check that every external URL in your sameAs array still resolves and still refers to you.

Measuring GEO Success

• Citation frequency: Manually test your core buyer questions in ChatGPT, Claude, Gemini, and Perplexity. How often does your business appear, and in which engines specifically? Track this on a schedule, not once.
• Share of Model: Of all queries in your category, what percentage cite you. This is the headline GEO metric.
• Brand Visibility Score (BVS): A composite metric weighting citations by placement and sentiment. A headline citation counts more than a footnote mention.
• Entity resolution: Confirm your sameAs links still resolve, and that any Wikidata item's P856 field still points back to your site. Both sides drift over time; a broken link is worse than no link.
• Referral traffic: Watch for visits from chatgpt.com, perplexity.ai, and claude.ai in your analytics. Not every citation drives a click, but the ones that do are worth isolating from regular traffic; we classify AI-agent visits as a separate category on this site for exactly that reason.
• Industry benchmarks: Conductor's 2026 AEO/GEO Benchmarks Report gives sector averages to compare your citation rate against.

Do I Need the AEO Guide Too?

Read the AEO guide for the extraction-layer half of this. Most businesses need both, in roughly the order their traffic mix suggests: whichever channel sends you more visitors today is worth optimising first.

The Bottom Line

GEO isn't a checklist you finish once. It's an ongoing bet that the model retrieving candidates for your category will keep finding genuinely useful, well-attributed, structurally clear content from your site and choose it over a competitor's. The businesses building that habit now, original data, named expertise, clean structure, will have compounding citation advantages once Share of Model becomes as contested as Google rankings are today.