01AEO, GEO and AI Overviews — What Each Term Actually Means
The three terms get used interchangeably, but they describe different surfaces with different mechanics.
| Term | What it refers to |
|---|---|
| AEO — Answer Engine Optimisation | Structuring content so it can be lifted as a direct, self-contained answer — originally for featured snippets, now for AI answer boxes generally |
| GEO — Generative Engine Optimisation | Optimising specifically for how generative systems (LLMs) select, synthesise and cite sources in a generated response |
| AI Overviews | Google’s specific generative summary feature at the top of search results — one implementation of GEO’s target surface |
In practice they’re all downstream of the same underlying discipline: making a source’s claims explicit, well-attributed and easy for a machine to lift without ambiguity. That’s the same discipline the rest of this knowledge base calls Semantic SEO — AEO and GEO are what it looks like when the retrieval surface is a generated answer instead of a results list.
02How Answer Engines Choose What to Cite
A generative answer engine does two things a traditional ranker doesn’t: it synthesises a response from multiple sources, and it decides which of those sources are trustworthy enough to cite by name. Both decisions favour the same source characteristics — explicit, unambiguous claims; a clearly identifiable author and entity; consistent facts corroborated across multiple pages and, ideally, multiple independent sites; and content structured so a specific passage answers a specific question without requiring the whole page to be read.
03Why Semantic SEO Was Already the Right Preparation
Sites that had already built genuine topical authority — explicit entities, corroborated claims, a coherent internal link network — didn’t need a separate “AI SEO” strategy when AI Overviews and chat-based search arrived. The signals answer engines use to select and trust sources are largely the same signals covered throughout the Semantic SEO guide: clear central entities, explicit predicates, and a source that consistently demonstrates the expertise it claims.
This is why the practice frames AI search as an extension of the existing discipline rather than a separate specialism to bolt on — the sites that struggle with AI visibility are almost always the same sites that were struggling with topical authority before AI Overviews existed.
04What LLM Visibility Adds on Top
There are a handful of things worth doing specifically for AI/LLM visibility, on top of standard semantic SEO practice:
- Self-contained answer passages — a paragraph that fully answers one specific question without requiring surrounding context, positioned near the relevant heading.
- Explicit entity and brand naming — LLMs draw on training data and retrieval together; being named clearly and consistently (not just implied) helps a model associate your brand with the topic.
- Structured data — schema markup gives an unambiguous, machine-readable version of the same claims made in prose, reducing the model’s uncertainty about what a page is asserting.
- Third-party corroboration — being mentioned or cited on other credible sites strengthens an LLM’s confidence in citing you directly, since it isn’t relying on your own claim about yourself alone.
05What AI Search Cannot Do For You
No technique reliably guarantees a citation in a specific AI answer — the systems are opaque, non-deterministic, and change frequently. Anyone promising guaranteed AI Overview placement is selling something the underlying technology doesn’t support. What a disciplined semantic SEO practice can do is put every structural and content signal in your favour, consistently, across the whole topical map — which is the same work that also earns traditional rankings, so it isn’t wasted effort even where a specific AI citation doesn’t materialise.