The Difference Between GEO, AEO And LLM SEO

Keep It Current and Say So Because retrieval happens at answer time, freshness carries real weight. A page updated this month can be cited this month, and a competitor can displace you simply by revising a page you have left alone for two years.

This is also where the most common own goal happens. A byline naming somebody who exists nowhere else is weaker than no byline at all, because it introduces a claim with nothing behind it. If you are going to name people, make sure they can be found.

None of this is a restoration of what was there before. It is an adjustment to a results page that now answers a portion of the questions itself, and the sooner the planning reflects that, the less painful each further change becomes. answer engine optimization

Generative Engine Optimization The broadest of the three in common use. It refers to being visible in systems that generate an answer rather than returning a list, which covers assistants, AI summaries on results pages and any interface that synthesises rather than links.

If your organic impressions held steady while clicks fell, you have probably met this already. An AI generated summary now sits above the results for a large share of informational queries, answers the question in place, and leaves the ten blue links below it with less to do.

That emphasis is worth watching, since retrieval is where most current influence actually lies. A proposal built primarily on getting into training data is describing a slower and far less controllable mechanism than one built on being retrievable now.

One warning worth stating plainly: none of this means writing for machines. Content that reads as if it were assembled for extraction tends to get treated as low quality by both readers and systems. The goal is writing that a person would find unusually clear and direct, which happens to be exactly what a model can quote. answer engine optimization

Now a growing share of those questions produce an answer instead of a list. The assistant reads the sources, forms the opinion and hands you a recommendation. The comparison step that used to happen in the buyer's head now happens inside a model, using sources the buyer never sees.

If your category still gets meaningful traffic from those, a proposal scoped only to assistants will leave that work undone. Conversely, if somebody proposes an answer engine optimization programme and delivers only snippet optimisation, they are working on the older half of the definition.

Some practitioners still use it that way, which makes it a superset of the newer work. Others use it as a synonym for the generative work specifically. Both usages are in circulation, which is why asking somebody what they mean by it is a reasonable question rather than a pedantic one.

Three acronyms, considerable overlap, and no governing body to settle the definitions. Different agencies use them differently, some interchangeably, and a few have invented a fourth to differentiate a proposal.

One further term worth watching for is any acronym an agency has coined itself. A proprietary framework name is not evidence of proprietary capability, and it is frequently a way to make comparison between proposals harder. The response is the same as for the established terms: ignore the label and ask which surfaces get measured, how often, and what evidence you receive.

Write between fifty and two hundred prompts covering five types: the category question, the problem question, the comparison question, the question that names a competitor, and the question that names you directly. The last one matters because it reveals what an assistant believes about you specifically, which is often more alarming than being absent.

Structure So the Boundaries Are Clear Headings that state what the section answers, short paragraphs, lists where the content is genuinely a list, and tables where the content is genuinely tabular. This is ordinary good structure, and it matters more than usual because it marks the edges of each self contained unit.

Identity work has an unusual property that makes it easy to undervalue: it improves everything else you do afterwards. Every mention earned after the details are consistent contributes to one record, while every mention earned before it may be filed somewhere it does nothing. Doing the tedious part first means the expensive part later actually accumulates, which reverses the order most programmes choose.

Extraction does not follow along. It takes the passage that answers the question, and a paragraph that spends four sentences setting up its point contains nothing extractable until the fifth. Put the answer in the first sentence and use the rest to qualify it.

One caveat worth writing on the report: any figure produced by a third party visibility tool is a sample from that tool's own prompt set and infrastructure, not a census. Attribute it to the tool by name whenever you quote it, and never present it as a count of what happened. answer engine optimization