What Is Generative Engine Optimisation (GEO)?

A working definition of GEO

Generative engine optimisation, usually shortened to GEO, improves the evidence signals that machine-assisted search systems can observe. The work is less about writing for a mysterious algorithm and more about publishing information in a form that is accessible, explicit, attributable, current, and useful.

A GEO programme should answer six operational questions. Can a crawler reach the material? Can the main claims be isolated from the surrounding prose? Is the source of each important claim clear? Does the page identify who produced it and when? Can systems determine which URL is canonical? Is there a concise passage that directly answers the user’s likely question?

GEO is not a guaranteed ranking trick

No independent tool can promise that a private model will quote a page, reveal undisclosed training data, or expose a vendor’s secret ranking formula. A responsible GEO audit distinguishes three evidence classes: direct observations from the content and crawl, live responses returned by configured providers, and comparative estimates based on disclosed content signals.

This distinction matters commercially. A marketing team can defend a decision such as “add the primary dataset beside this statistic” because the missing source is observable. It cannot defend “this change guarantees a 23% citation increase” unless a controlled experiment actually measured that outcome.

The six signal groups professionals can act on

1. Retrievability. Status codes, crawler access, canonical URLs, sitemap presence, internal links, readable document text, and redirect behaviour.
2. Authority. Named authors, publisher identity, primary-source links, methodology, expert review, and evidence provenance.
3. Specificity. Clear entities, dates, units, comparisons, definitions, and answer-sized factual passages.
4. Structure. Descriptive headings, logical sections, lists, tables, semantic HTML, and structured data that matches visible content.
5. Freshness. Accurate publication and update dates, maintained references, current statistics, and explicit coverage periods.
6. Transparency. Ownership, limitations, source methodology, corrections, commercial relationships, and clear separation of fact from estimate.

A practical GEO workflow

Start with a complete inventory, not a single flagship article. Crawl the website, record every canonical page, and classify failures. Next, extract claims and map them to adjacent sources. Compare page templates so that repeated problems—missing author fields, inconsistent dates, blocked PDFs—become system fixes rather than a long list of manual edits.

Then prioritise by opportunity and effort. A page already ranking for a valuable topic but missing direct evidence may deserve attention before a low-value page with twenty structural faults. Assign an owner, define the target condition, and specify the recrawl test that will prove the change was implemented.

How GEO should be measured

Use a score as a navigation aid, not as the final truth. The useful layer is underneath: pages affected, claims extracted, sources missing, crawl failures, live query observations, competitor domains returned, and changes between dated snapshots. Track whether remediation moved those facts.

For live AI observations, preserve the exact query, provider, model, timestamp, citation URLs, target position, and response context. A citation is an observation at one moment, not proof of model training or a permanent ranking.

A 30-day implementation plan

• Week 1: establish the site inventory, canonical map, sitemap coverage, and blocked-page list.
• Week 2: repair authorship, dates, structured data, source adjacency, and answer clarity on the highest-value pages.
• Week 3: publish missing evidence pages and improve internal links from relevant hubs.
• Week 4: recrawl, compare the new snapshot, run the same live query set, and record what changed.

One discipline, many names: GEO, AEO, LLMO, AI SEO

The practice this guide describes travels under several names, and the differences between them are smaller than the vendors coining them imply. Answer engine optimisation (AEO) is the most common alternative, emphasising being the direct answer to a question; generative engine optimisation (GEO) emphasises being a cited source inside a generated response. In day-to-day work they are the same discipline, and IndexHalo measures both: the same crawl, evidence and citation checks serve either framing.

LLMO (large language model optimisation), AI SEO, LLM SEO and generative search optimisation (GSO) are broader umbrella labels for the same shift — content optimised for retrieval and citation by AI systems rather than only for ranked links. If you search for any of these terms, the advice you find should look substantially like this guide; where it promises secret levers instead, treat it with the scepticism described below. The glossary keeps working definitions for each term.

A worked example: the same page, twice

Abstractions are easy to nod along to, so consider a concrete case. A software company publishes a comparison page titled “Our platform vs the alternatives”. It ranks respectably in classic search and receives steady traffic. It is also almost impossible for an answer engine to use, and the reasons are instructive.

The page opens with two paragraphs of positioning before any substantive claim. Comparative data lives in an image — a screenshot of a table exported from a spreadsheet — so the numbers are invisible to any text extractor. Pricing is described as “competitive” rather than stated. There is no author, no publication date, and no link to the methodology behind the comparison. Nothing on the page is false; it is simply unusable as evidence.

Rebuilt for retrieval, the same argument survives intact. The comparison becomes an HTML table with real cells. Each row cites the source and the date the figure was checked. The opening paragraph states the conclusion in two sentences that make sense in isolation, because a passage lifted into an answer arrives without the surrounding page. Pricing is a number. The methodology becomes a linked page describing how the comparison was run and what it excludes.

The rewrite is not longer and it is not more promotional. It is more quotable: every material claim is now a self-contained, attributable statement that an engine can lift without distorting it. That is the whole discipline in one page.

Five failure modes we see repeatedly

The accidental block. Someone adds User-agent: GPTBot / Disallow: / to decline model training, and takes OAI-SearchBot and ChatGPT-User down with it because all three were grouped under a wildcard. The site is now invisible in ChatGPT answers, and nobody notices for months because no dashboard reports it. Our crawler access checker resolves this in seconds.

Evidence trapped in images. Charts, tables and infographics carry the most valuable claims on a page and the least extractable ones. If a number matters, it needs to exist as text somewhere on the page.

The undated page. An engine deciding between two sources will discount the one that cannot demonstrate currency. Pages with no visible date, no dateModified, and no internal signal of recency lose comparisons they would otherwise win.

Buried conclusions. Journalistic structure — context, narrative, then the point — is a liability when passages are retrieved individually. State the finding, then support it.

Unattributed assertion. “Studies show that…” with no link is worth nothing to a system whose main job is deciding which source to trust. Name the study and link it.

What GEO is not

It is worth stating the negatives plainly, because the category attracts confident claims that do not survive scrutiny.

GEO is not a way to make a model “prefer” your brand. Nobody outside the labs knows the retrieval weights, and anyone selling certainty about them is guessing. It is not keyword stuffing for a new audience — passage-level retrieval punishes repetition more heavily than classic ranking did. It is not a replacement for authority: a well-structured page from an unknown source still loses to a well-structured page from a recognised one.

And it is not a one-off project. Model behaviour, retrieval strategies and crawler policies all change. A page that is cited today can be dropped next quarter because a competitor published better evidence, not because anything on your site got worse. That is why measurement has to be continuous and why observations need timestamps.

The bottom line

Good GEO does not replace good SEO, research, or editorial judgement. It makes their output easier to retrieve and defend. The strongest programme combines technically accessible pages, original evidence, precise writing, transparent sourcing, and repeatable measurement.

If you take one idea from this guide, take this: an answer engine is not deciding whether your page is good. It is deciding whether your page is usable as evidence for a specific question asked by a specific person. Those are different tests, and the second one is largely mechanical — which means it is largely fixable.

Common questions

Is GEO just SEO with a new name?+

No, but the overlap is larger than the marketing suggests. GEO inherits the entire technical foundation of SEO — crawl access, canonicals, architecture, performance, authority — and adds passage-level evidence analysis, provenance requirements and citation measurement. If your SEO is broken, your GEO is broken; the reverse is not true.

How long before GEO work shows results?+

Structural fixes such as restoring crawler access can change citation behaviour within days, because the constraint was mechanical. Content and evidence work operates on the timescale of re-crawling and competitive displacement, which is realistically weeks to months. Anyone promising a fixed timeline is describing a hope, not a mechanism.

Do I need to publish an llms.txt file?+

It costs very little and removes ambiguity for the agents that read it, so it is usually worth doing. It is not a ranking factor and it is not read by every engine. Treat it as cheap hygiene rather than a lever.

Can I stop AI systems using my content?+

You can decline the published training crawlers in robots.txt, and the major AI companies honour that. You cannot retroactively remove content from models already trained, and robots.txt is a voluntary protocol rather than an access control. Anything genuinely sensitive belongs behind authentication.

Does GEO only matter for large publishers?+

The mechanics are size-independent. A small site with clear structure, dated pages, named authors and well-sourced claims is frequently cited over a large one without them, because the engine is assessing usability as evidence rather than domain size.