What is generative engine optimization (GEO)?

The short definition

GEO is the practice of improving how accurately, and how often, your company appears in answers written by AI systems such as ChatGPT, Perplexity, Google's AI Overviews and AI Mode, Gemini, Claude and Microsoft Copilot. Instead of competing for a position in a list of ten blue links, you are competing to be the source an AI system draws on, quotes, or names when it writes a single synthesized answer.

It helps to split the goal into two parts. The first is being findable: the systems that feed AI answers must be able to crawl, index and retrieve your pages. The second is being usable: once your page is retrieved, its content has to be clear, specific and structured enough that a model can lift an accurate statement from it and attribute it to you. Most practical GEO work is one of those two things.

The phrase "generative engine optimization" was popularized by a 2023 academic research paper that studied how content changes affect visibility inside generative search responses (see the sources below). The paper is a useful starting point, but it was run in a controlled research setting; it does not tell you how ChatGPT, Perplexity or Google behave on your specific pages today.

GEO, AEO and LLMO: which term should you use?

You will see several overlapping labels. Nobody governs these terms, so usage varies between writers and vendors. This is how they are commonly used:

In practice the page-level work overlaps almost completely, so Venoh uses GEO and AEO interchangeably. What matters is the underlying activity, not the acronym. See the glossary for short definitions of every term used on this site.

How AI answer engines pick sources

Each product works differently and changes often, and the vendors do not publish complete details. A simplified picture that fits most search-connected assistants looks like this:

1. Interpret the question. The system works out what is being asked. Some systems split one question into several searches to cover different angles.
2. Retrieve candidates. It pulls pages or passages from a search index or by fetching pages live. If your page is not in the index, or the crawler was blocked, it cannot be retrieved.
3. Filter and rank. Candidates are scored for relevance and, in ways the vendors do not fully disclose, for signals of quality and trust.
4. Generate a grounded answer. A language model writes the answer using the retrieved passages as its raw material. This step is called grounding.
5. Attach citations. Many products show which sources supported the answer. Not all do, and a source can influence an answer without being shown.

There is a second route that involves no retrieval at all: a model can repeat something it absorbed during training. That route is slow to change and gives you no citation trail. Most of what a company can actively influence today sits on the retrieval route, which is why crawlability and clear on-page content come first.

What tends to influence whether you get cited

The following factors are widely discussed by practitioners and consistent with how retrieval-based systems work. Treat them as well-reasoned working assumptions rather than proven ranking factors; we say more about that below.

1. Crawlability and indexability

If AI-related crawlers cannot fetch your pages, nothing else matters. Common blockers are a blanket Disallow in robots.txt, a firewall or CDN bot-protection rule that challenges automated visitors, and content that only appears after JavaScript runs. Our how-to guide lists the specific crawler names and how to test access.

2. Answer-first, extractable content

Retrieval systems work on passages. A heading that states a question, followed immediately by a direct two-to-three sentence answer, is easy to lift. The same information buried inside a long marketing paragraph is not. Lists, short tables and definitions are all easy for both people and machines to extract.

3. Structured data and clear entities

Structured data (schema.org markup, usually written as JSON-LD) states facts about a page in a machine-readable form: who the organization is, what a product costs, which questions a page answers. It helps parsers and search indexes understand a page unambiguously. Whether it directly changes what a language model writes is not established by public evidence, and Google states that its AI features need no special schema.org markup. What it does is remove guesswork at the retrieval and indexing stages, and it is inexpensive to do properly.

4. Specific, verifiable facts

Concrete statements (what you do, who it is for, what it costs, what you do not do) give a model something safe to repeat. Vague superlatives give it nothing. If a claim needs a number or a source to be believable, provide it and show where it comes from.

5. Corroboration elsewhere on the web

Systems that cross-check tend to trust facts that several independent sources agree on. That means consistent descriptions of your company across your own site and third-party pages such as directories, publications, partner pages and reviews from real customers. This is the slowest factor to improve and the hardest to fake, which is exactly why it is credible.

6. Freshness and consistency

Out-of-date pricing, conflicting product names or two different descriptions of the same offer make it harder for a system to state anything confidently about you. Keeping key facts current and identical everywhere is unglamorous and effective.

What is uncertain or unproven

This field is young and the systems are black boxes. Anyone selling GEO should be able to say what they do not know. Here is our list:

• Ranking factors are not published. Every claim about "what AI engines reward" is inference from experiments, observation or first principles.
• Answers vary. The same prompt can produce different answers on different days, in different countries, or on repeated runs. A single test proves very little.
• Engines differ. What helps in one product may do nothing in another, and each changes without notice.
• Schema's direct effect on language models is unproven. It is good practice for machine readability, but we cannot show that adding it makes a specific model cite you.
• llms.txt is a proposal, not a standard. Support by AI providers is not guaranteed; check its current status before relying on it.
• Vendor studies have limits. Correlations found in a company's own dataset can reflect how that dataset was built. Read the method, not just the headline.

A practical order of operations

1. Confirm the right crawlers can reach your pages, and that the content is in the HTML rather than produced only by JavaScript.
2. Make your basic entity facts (name, description, offerings, pricing, contact) identical and easy to find across your site.
3. Publish answer-first pages for the questions your buyers actually ask, in the wording they use.
4. Add structured data that matches what is visibly on each page, and nothing that is not.
5. Earn independent mentions through directories, publications, partnerships and genuine customer reviews.
6. Measure with a fixed set of buyer prompts, repeated over time, and record what you see.

The how to get cited by AI checklist turns each step into concrete tasks with a way to verify it. If you would rather have the first pass done for you, read how the Venoh diagnostic works and what it costs.

Who should care about GEO

GEO matters most for businesses where buyers research and shortlist suppliers before they ever speak to anyone, which describes many B2B purchases. If people can ask an AI assistant "what are the best options for X" or "how does Y compare with Z" and act on the answer, then whether the assistant can find, understand and correctly describe your company is a genuine commercial question. It matters less for businesses whose customers arrive almost entirely through other channels, though the underlying hygiene (clear pages, accurate facts) is useful regardless.