Terms you'll need
AI visibility
Being mentioned, cited, or recommended by AI answer engines when someone asks a relevant buying question. It is the outcome GEO and LLM SEO work is aimed at.
GEO (Generative Engine Optimization)
The practice of making brand and content information usable and citable by AI answer systems, so a model has clear, verifiable evidence to draw on when it forms an answer.
AEO (Answer Engine Optimization)
A close cousin of GEO, focused on structuring content to directly answer questions. AEO's content work overlaps heavily with GEO — the same clear, direct answers help both.
LLM SEO
An informal term some practitioners use interchangeably with GEO. It describes the same underlying work from the angle of "optimizing for the model" rather than "optimizing for the answer."
AI Overviews
Google's AI-generated summary shown above traditional search results. This product does not currently measure AI Overviews — treat any claim of coverage there with caution.
AI Mode
Google's fuller conversational search experience. Like AI Overviews, AI Mode is not currently measured by this product.
What stays standard SEO, and what changes
Technical health, on-page relevance, and link-earned authority remain the foundation. Crawlable pages, solid Core Web Vitals, clear on-page signals, and a credible backlink profile do not stop mattering because AI answer engines exist — they are still the base every other layer builds on.
What changes is the measurement target and the bar for evidence. Classic SEO optimizes for rankings and clicks on a results page. LLM SEO and GEO optimize for whether an AI answer mentions, cites, or recommends you — a different endpoint that depends on different signals.
The evidence a model weighs is also stricter than what wins a page-one ranking. A page can rank well with persuasive but vague copy. An AI system needs structured, citable claims that are corroborated by third-party sources before it treats your brand as a safe answer to surface.
On-site evidence
Start with entities: say clearly, in plain language, what your brand, product, or service is called and what category it belongs to. A system that cannot name what you are cannot recommend you with confidence.
Pair that with original expertise — content only you could have written, grounded in your own data, methodology, or experience — and specificity. Named use cases and concrete constraints beat generic claims like "we help businesses grow."
Structure your answers directly: lead with the answer, then support it, rather than burying it in narrative prose. Depth of documentation and visible freshness (dated updates, current facts) round out the on-site signals a model can draw on.
Off-site evidence
Your own site is not the only source an AI system reads. Third-party reviews, editorial or press coverage, directory listings, and community mentions on forums and Q&A sites all corroborate — or contradict — what your site claims.
This corroborating evidence often matters more for AI visibility than it did for classic SEO, because these systems are explicitly trying to validate a claim against independent sources before repeating it. Keeping your public footprint accurate and consistent across all of these surfaces is part of the work, not an afterthought.
Measurement and auditing workflow
At a high level, the audit loop looks like this: set the scope of what you are measuring, map the buyer prompts your audience actually uses, capture the evidence behind each answer, score the underlying signals separately rather than as one blended number, then prioritize fixes based on what is weakest. The audit guide below walks through this framework in full detail — this page focuses on the model behind it, not the step-by-step process.
What this can't guarantee
No technical file, schema markup, or single tactic guarantees a citation or recommendation from any AI engine. Anyone who tells you llms.txt, a specific meta tag, or a particular content format will make an AI system cite you is overstating what is currently known.
These are probabilistic systems. The same brand, the same page, and the same underlying facts can produce a different answer depending on the exact prompt wording, the time it is run, and the model version behind it. The work described on this page improves your odds of being usable evidence — it does not produce a fixed outcome, and no one who tells you otherwise is being straight with you.
Frequently asked questions
Is GEO the same as SEO?
No, but they share a foundation. SEO optimizes for rankings and clicks on a search results page. GEO optimizes for whether an AI engine mentions, cites, or recommends you. GEO builds on the same technical and on-page fundamentals SEO relies on — it does not replace them.
How long does GEO work take to show results?
There is no fixed timeline. AI engines update their models and crawl the web on their own schedules, which are not published or predictable. Treat GEO as ongoing work you measure repeatedly, not a project with a guaranteed completion date.
Do I need llms.txt?
llms.txt is an emerging, unstandardized signal that some sites publish. It has not been shown to affect whether AI engines cite you. It is reasonable to add if it costs you little, but do not treat it as a requirement or expect it to move your visibility on its own.
Where do I start?
Run the free checker to see where you stand today, then use the audit guide to build a repeatable measurement workflow. Come back to the on-site and off-site evidence sections on this page as your working checklist for what to fix.