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Why a model names one business and not another.
This page is the reasoning rather than the offer. Where the term came from, how these systems actually assemble an answer, and which of the popular tactics follow from that and which do not.
Short answer
Generative engine optimization is the practice of improving how likely a generative AI system is to use and cite your content. The term was introduced in a 2023 academic paper. In practice it divides into two mechanisms: what a model absorbed during training, which you cannot change, and what it retrieves live when answering, which you can influence.
Two mechanisms, not one
Retrieval vs training
Sourced, not speculated
Written to be quoted
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01 Origins
Where the term comes from.
Generative engine optimization entered the vocabulary through a 2023 research paper of that name, by authors from Princeton, the Georgia Institute of Technology, the Allen Institute for AI and IIT Delhi. The paper studied what changes to a source page altered its visibility inside generated answers, and found that additions such as quotations, statistics and cited sources tended to help, while conventional keyword optimisation did comparatively little.
That research matters mainly because it is one of the few things in this field that was actually measured rather than asserted. It was conducted on a specific generative search setup at a specific time, so it should be read as evidence about how these systems behave rather than as a permanent rulebook — but it is a great deal more than most GEO advice rests on.
Since then the term has been adopted by the marketing industry, where it is often used loosely to mean any activity involving AI and search. That looseness is why this page exists: the mechanism is specific, and understanding it tells you which of the popular tactics are reasonable and which are decoration.
02 The mechanics
Two mechanisms that get confused.
Almost every argument about GEO comes from conflating these. They behave completely differently.
Training — fixed until the next model
What a model learnt when it was built. If you were not well described on the web then, nothing you publish today changes it. This is why new businesses are invisible to models and why patience is not optional.
Retrieval — live, and influenceable
When an assistant searches the web to answer, it fetches and quotes pages now. This is where your work actually lands, and it is why crawler access and current, clear pages matter.
Corroboration — the shared factor
Both mechanisms favour entities described consistently in several independent places. It is the one investment that pays into both, and the one nobody can shortcut.
03 Implications
What follows from the mechanism.
Each of these is a consequence of how retrieval-augmented answering works, not a guess about a ranking factor.
Practical implications
- Be retrievable. If retrieval crawlers cannot read your page, you cannot be quoted in a live answer. This is a robots.txt decision people make carelessly.
- Be self-contained. A retrieved page is read in isolation, without your navigation or context. A page that only makes sense in sequence is a poor source.
- State facts explicitly. Models quote specifics. A page of positioning language has nothing to lift; a page with a range, a condition or a defined process has.
- Attribute your claims. Sourced statements are safer for a model to repeat, which appears to make them more likely to be used.
- Be consistent everywhere. Contradiction between your site and your profiles gives a model reason to avoid naming you at all.
- Keep it current. Retrieval favours pages that appear maintained. A visible review date is cheap and genuinely useful.
- Write something worth quoting. The unavoidable one. Generic service copy is not cited by anything, because there is nothing in it to cite.
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The reasoning above is public because I would rather be judged on it than on a claim. If you want it applied — entity audit, schema, crawler policy, citable content — that is the service.
- The method is published, deliberately
- Sourced claims, flagged uncertainty
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04 Straight talk
Tactics that follow, and tactics that do not.
Sorted by whether the mechanism above actually supports them.
Supported by the mechanism
- Specific, verifiable content. Directly what retrieval favours, and what the research found.
- Entity consistency and sameAs. Reduces ambiguity about who you are across sources.
- Deliberate crawler policy. A concrete lever, entirely within your control.
- Third-party mentions. Corroboration is the one thing a model can check independently of you.
Not supported, or unproven
- Keyword density for AI. The research found conventional keyword optimisation did comparatively little here.
- llms.txt as a ranking factor. A 2024 proposal with limited confirmed adoption. Cheap to add; not an established mechanism.
- "Submitting" to ChatGPT. No such route exists. Any service claiming it is selling something else.
- AI visibility scores. Composite numbers with no published basis. Ask what the denominator is.
05 Scepticism
How to test any GEO claim.
Five questions that dispose of most of what is currently being sold.
- What is the mechanism?
- If the answer is not training or retrieval, ask again. Everything real routes through one of the two.
- Where is the source?
- A paper, a platform announcement, or a documented crawler. "Industry data" without a citation is not a source.
- Can it be measured?
- If the only evidence is a proprietary score, it cannot be independently checked. That should lower your confidence.
- Would it help anyway?
- Consistent descriptions and clear content help conventional search too. Tactics that only make sense if the AI theory is right are the risky ones.
- What happens at the next model update?
- Anything dependent on a current quirk will stop working. Anything based on being genuinely credible will not.
— AI & Next-Gen Search
Related services.
These sit next to generative engine optimization and are usually bought with it. Same person doing the work in each case.
— Questions
generative engine optimization — your questions.
Straight answers, including the ones that cost me work. If yours is not here, ask it — I reply the same day.
From a 2023 academic paper titled "GEO: Generative Engine Optimization", by researchers at Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi. It studied which changes to a source page altered its visibility inside generated answers. The marketing industry adopted the term afterwards, considerably more loosely.
AEO is usually about answer surfaces inside a search engine — featured snippets, People Also Ask, AI Overviews. GEO is usually about generative assistants such as ChatGPT, Gemini and Perplexity. They overlap heavily, and the distinction is more useful as a way of separating the work than as a hard boundary.
Not on demand. Training happens at intervals on a corpus you do not control, and by the time a model ships, that corpus is fixed. What you can do is be well described and widely corroborated now, so that the next training pass has good information about you. It is a slow investment, and it is the only honest version.
That is what the 2023 research found, on the systems it tested at that time. It is consistent with how retrieval-augmented answering works: specific, attributable content is easier to quote safely. Treat it as good evidence rather than a permanent law, and note that it also happens to make content better for humans.
It costs ten minutes, so there is little reason to refuse. But it is a community proposal from 2024 with limited confirmed adoption by the major engines, not a standard they have committed to. Add it as a cheap hedge; do not restructure your site around it.
In the durable parts, yes — consistent descriptions, correct schema, a deliberate crawler policy and one genuinely citable page. Those help conventional search regardless, so the downside is nil. A large dedicated GEO budget for a small business is, in my view, premature, and I will say so rather than take it.
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Judge the reasoning, then decide.
The method is on this page because I would rather be judged on it than on a promise. If you want it applied to your site, start with the audit — and you keep it either way.
- Sourced claims, flagged uncertainty
- No invented visibility scores
- I will tell you when it is premature
- Audit is yours whether you hire me or not
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