What is generative engine optimization? How we explain GEO to clients
Generative engine optimization (GEO) means getting named and cited in AI answers. Here is how we explain it, what it involves, and how we run it for clients.
When a new client asks us what generative engine optimization is, we give them a one-line answer: GEO is the work of getting your brand named and cited in the answers AI engines write. Then we show them an example. We type a buying question into ChatGPT and let them see which brands appear, and which sources sit behind them. That demo does more than any definition.
Where the term comes from
"Generative engine optimization" was introduced in a 2023 academic paper, later presented at KDD 2024, which tested content changes on a benchmark of generative search queries. We will not repeat its percentages, since they came from a research setup, not your market. We do take the main lesson from it: how content is written and sourced affects whether an engine uses it.
How an AI answer gets built
The process differs between engines, but the general flow is this. A user asks a question. The system may split it into several narrower searches, often called query fan-outs. It retrieves pages for each, a model reads what came back, and the answer is written with citations. You can be left out at any step: the crawler could not reach you, your page did not answer the sub-question, or other sources described you better.
GEO versus SEO
Clients often ask whether this is just SEO with a new label. Our honest view: partly. The foundations carry over. A crawlable site, clear pages and genuine authority matter in both. The differences are in what you measure and which sources matter.
- AI answers vary from run to run, so you track a panel of prompts over time, not a single rank.
- Third-party sources such as forums, video and review pages carry a lot of weight in many categories.
- The success signal is a mention or citation, not a position on a list.
- AI crawlers are a separate audience, with their own access and error patterns.
AEO, answer engine optimization, is used almost interchangeably. We treat it as the same work.
What a GEO program includes
We organize a program into five parts.
Prompts. We build a set from sales calls, support tickets and search data, and tag by topic, persona and country. In Promptwatch, Prompt Explorer can suggest prompts from Search Console data, ranking keywords or People Also Ask, and persona stacking runs one prompt as several buyer types.
Measurement. Promptwatch runs daily panels per model, country and language, and reports Visibility Score, Share of Voice, Sentiment and Position in Answer. It is the platform we run client programs on.
Diagnosis. Citation analytics show which pages and domains engines cite, including Reddit, YouTube and LinkedIn, and offsite mentions. Agent Analytics reads IP-verified AI crawler logs for bots such as GPTBot, OAI-SearchBot and ClaudeBot, with error tracking and crawl-to-citation paths. Crawler logs start on Professional.
Content. We write answer-first pages for the sub-questions. Content gap analysis shows coverage by prompt. On Business, the Content Agent can plan and publish against gaps, and we keep its review inbox on.
Attribution. Visitor analytics shows AI-referred traffic and conversions, which is what the client's finance team cares about.
What we tell clients GEO cannot do
It cannot guarantee a recommendation. We say so on the first call. Engines make their own choices, and answers vary. GEO increases the odds and gives evidence about what is working. We also avoid setting a visibility target as a promise. The direction over time, relative to competitors, is the honest measure.
What the first month looks like
Week one: prompt set, Brand Book, baseline. Week two: check crawler logs, fix access errors. Week three: read citations, identify the largest gap. Week four: ship the first fix. Then we re-measure and repeat. It is not glamorous, and it works because each step is something we can verify in data.
Common mistakes
- Tracking too few prompts and drawing conclusions from one run.
- Ignoring crawler access.
- Writing generic content with no sourced specifics.
- Skipping off-site presence.
- Changing too many things at once.
Our advice if you are starting
Learn the loop before buying anything: prompts, daily measurement, citations, crawler access, fixes. Then choose a tool that covers the whole loop, not just the first step. A monitoring-only tracker will tell you where you stand. It will not tell you why, and that is the part you will actually need.