Generative Engine Optimization (GEO)

A generative engine answers a question by writing prose. It reads sources, synthesizes them, and names a few. There is no list to climb and no position ten to settle for — you are quoted or you are absent.

GEO is the practice of being quoted. In day-to-day work that means three things: knowing which questions your buyers actually put to these engines, measuring how often you appear in the answers, and changing what the engines can find so the number moves.

The name is contested and the distinction is not worth your time. GEO, AEO, LLMO, and generative SEO all label the same work. GEO is the term that came out of the academic literature; AEO is the term that came out of the SEO industry. They describe identical practice.

In marketing, GEO now has two unrelated meanings, and searches for the term return both.

The older meaning is geographic: geo-targeting, geo-fencing, geo-modified keywords — anything scoped to a physical location. If you arrived here looking for local search, that is a different discipline entirely.

The newer meaning, and the subject of this guide, is generative engine optimization. It has nothing to do with location. The collision is unfortunate and shows no sign of resolving, which is part of why many practitioners prefer AEO.

GEO and AEO are the same thing. The real comparison is between generative optimization and classic SEO.

Five surfaces account for nearly all of it, and they behave differently enough that a brand can dominate one and be invisible in another.

The tactics with evidence behind them are unglamorous and mostly not on your own domain.

Two things absorb a disproportionate share of GEO effort and have little evidence behind them.

The first is llms.txt. It is a sensible proposal and costs nothing to publish, but no major engine has committed to consuming it, and there is no measurable citation lift attributable to it. Publish one if you like; do not build a strategy on it.

The second is keyword-stuffing for models. Generative engines are not matching strings. Writing 'best AEO tool' fourteen times does not make you the best AEO tool, and the resulting prose is worse for the humans who do arrive.

The measurement problem is the discipline. Because engines sample, a number from a single run is indistinguishable from chance, and most disagreements about AI visibility are really disagreements about run counts.

A defensible setup has four properties: a prompt set written the way buyers actually ask; enough repeated runs per prompt that the rate is stable; engines reported separately rather than averaged; and access to the raw answers, so a surprising number can be explained rather than merely observed.

Track mention rate, citation rate, share of voice against named competitors, and the descriptions themselves. If a tool gives you a single composite score and no way to see how it was computed, you cannot debug it when it moves.

The category is crowded and young. Most tools do the same core job — schedule prompts, run them across engines, log what came back — and differ in engine coverage, sampling depth, and how much of the raw data they let you see.

Two questions cut through the marketing. How many times is each prompt actually run, and can you read the answer behind the score? A tool that samples thinly will report movement that is not there, and a tool that hides the raw output cannot be checked when it does.

Elmo is open source for exactly this reason. Self-host it, read the code that computes the score, and verify the methodology rather than trusting it.