Three Acronyms, One Goal
GEO (generative engine optimization), AEO (answer engine optimization), and LLM SEO are largely the same pursuit under different labels: being the source an AI-generated answer draws on.
AEO is the older term, inherited from the era of featured snippets and voice answers. GEO arrived with generative systems such as AI Overviews, AI Mode, ChatGPT, and Perplexity. LLM SEO is the same idea named after the technology. People do draw finer distinctions, but in practice the differences are vocabulary rather than method, and vendors have a commercial interest in making them sound like separate disciplines requiring separate products.
Google's own position, published in May 2026, is that these are still SEO. There are no additional requirements and no special optimizations to appear in its AI features. Any framework worth following has to start from that.
The Myths Worth Deleting
An earlier version of this post recommended some of these. The 2026 guidance makes them untenable, so here they are with Google's actual position:
| Common advice | What Google says |
|---|---|
| Publish an llms.txt | You do not need new machine readable files; Google Search does not use them |
| Add schema to earn AI citations | Structured data is not required for generative AI search, and there is no special markup for it |
| Break content into small chunks | There is no requirement to break your content into tiny pieces |
| Optimize the sentence, not the page | No such requirement exists; page-level quality guidance is unchanged |
| Hit a target word count | There is no ideal page length |
| Write in a distinct AI-friendly style | You do not need to write in a specific way just for generative AI search |
Structured data remains worth implementing for rich results and entity clarity. It is the causal claim about citations that does not hold.
What Is Left, and Why It Works
1. Topic coverage, because of query fan-out
Google documents that its AI features use query fan-out: the system issues multiple related searches across subtopics and data sources, then synthesizes across the results. More supporting pages are identified during generation than in a classic search, which is why a wider set of links can appear.
This is the strongest structural argument for topic clusters, and it is a mechanical one rather than a vague appeal to authority. If the system is silently running a dozen subqueries around the question, every subquery you cover well is another chance to be pulled in. Comprehensive coverage of a defined topic beats a single excellent orphan page.
2. Clear answers, because they are easier to use correctly
State the answer near the top of the section that promises it, then support it with detail and evidence. Keep important claims self-contained enough that quoting them does not distort them.
The reason is not that a model scores your paragraphs individually. It is that ambiguous writing gets summarized badly by every reader, and a clear claim is one a system can reproduce without misrepresenting you. This is the same advice good editors have always given.
3. Non-commodity content, because it is the actual differentiator
Google's guide asks for content that is not simply recycled from what is already indexed, and that a generative model could not have produced on its own. First-hand experience is the example it gives.
This is the hard one, and it is where most GEO programmes quietly fail. If your page restates publicly available facts in a new order, no structural optimization makes a synthesis engine prefer it over the dozen pages that did the same thing first. Original data, direct testing, named expertise, and a real point of view are what is left.
4. Trustworthy presentation, because it is checkable
Attribute statistics to their primary source and link to it. Show who wrote the piece and why they are credible. Date your content and update it when it stops being true. None of this is an AI tactic; it is the substance behind the E-E-A-T guidance, and it is verifiable in a way that self-declared authority is not.
The Off-Google Complication
The framework above concerns Google, where the mechanism is documented. Other assistants are less transparent, and the honest summary is that less is known.
Independent analyses through 2026 consistently report two things. First, brand mentions across third-party high-trust domains correlate more strongly with LLM citation rates than backlinks do. Second, ChatGPT runs a live web search on only a minority of queries, answering the rest from training data. Taken together, that suggests a meaningful share of assistant visibility is determined off your site and before your latest edit, and is not addressable by on-page work at all.
The implication is not that on-page work is pointless. It is that for assistant visibility specifically, presence in your category's press, review sites, comparison round-ups, and communities does work your own CMS cannot. Google's caution about inauthentic mentions applies here too: manufactured presence is both detectable and beside the point.
Measuring Any of This
Decide up front which claims you will treat as measured and which as inferred, and never blur them in a report.
Measured, on Google: the Search Console generative AI performance reports, available since June 2026, and direct SERP observation of whether an AI Overview appeared and whether you were cited. AIORadar's AI Overview Tracker covers the second, recording each observation with its query, market, device, and check time.
Measured, off Google: referral visits and conversions from recognized AI assistants in connected GA4 data. This tells you an assistant sent someone, which is an outcome worth having, but it is not the same as reading the answer that sent them.
Inferred: essentially everything else, including the inside of ChatGPT and Perplexity answers. Tools that present that as observed data are overstating what is knowable. Set your targets against the measurable things, and treat the rest as directional.