AI search has created a vocabulary problem. Some terms describe real changes in how people find information. Others are loose labels for work SEO teams were already doing. A few are speculative enough that you should understand them before spending money on them.
This glossary explains the terms you are most likely to see in agency proposals, SEO tool dashboards, AI-search reports, and marketing articles.
For the older vocabulary, keep SEO in Plain English nearby. This page focuses on the newer answer-engine and AI-search layer. For a side-by-side comparison of the main acronyms, see SEO vs AEO vs GEO.
Core terms
SEO (search engine optimization). The practice of improving a website so search engines can find, understand, and rank it. SEO still handles the base work: crawlability, indexation, content, links, technical quality, and user experience.
AEO (answer engine optimization). Structuring content so a search system can select a direct answer. AEO is useful for featured snippets, People Also Ask, voice assistants, FAQ-style results, and AI-generated answers that need a short passage.
GEO (generative engine optimization). Improving content so it can be included, cited, or represented accurately in generated AI answers. GEO usually focuses on source quality, original examples, evidence, authorship, and clear claims.
LLMO (large language model optimization). Helping language models discover, understand, and describe your business, product, or content accurately. It overlaps with GEO, but often pays more attention to brand representation across AI tools.
AI SEO / AI search optimization. The umbrella term for SEO work aimed at AI-powered search experiences such as Google AI Overviews, Google AI Mode, ChatGPT Search, Copilot, Perplexity, and Gemini.
SXO (search experience optimization). Improving the experience after someone finds you through search. It connects result expectations, page speed, readability, trust, and conversion.
AI answer terms
Generated answer. An answer written by an AI system in response to a user’s prompt or search. It may summarize several sources, cite pages, recommend products, or answer follow-up questions.
AI Overview. Google’s AI-generated summary that can appear in Search for some queries. It may include links and follow-up paths, depending on the query and market.
AI Mode. Google’s more conversational search experience for asking follow-up questions and exploring a topic through AI-generated responses.
AI citation. A source link shown with an AI-generated answer. Example: an answer about SEO audits cites a guide from your site.
Zero-click search. A search where the user gets enough information without clicking through to a website. Featured snippets, calculators, knowledge panels, local packs, and AI answers can all create zero-click behavior.
Grounding. Connecting an AI answer to retrieved evidence or sources. A grounded answer is easier to check because it points back to the information used.
RAG (retrieval-augmented generation). A method where an AI system retrieves outside information before generating an answer. That retrieval step lets the system use current webpages, documents, or databases instead of relying only on training data.
Entity and meaning terms
Entity SEO. Helping search systems understand a real-world thing: a business, person, product, place, event, or organization. Consistent names, profiles, schema, and third-party references all help.
Semantic SEO. Covering the meaning around a topic instead of repeating one exact keyword. A strong page about home loans naturally covers deposits, repayments, rates, borrowing limits, lenders, and eligibility.
Topical authority. The trust a site builds by publishing a connected body of useful content on one subject. It is an industry concept, not a public Google score.
E-E-A-T. Experience, expertise, authoritativeness, and trustworthiness. It comes from Google’s Search Quality Rater Guidelines and helps explain what credible content tends to show: real experience, qualified authors, reputation, sources, and safe advice where stakes are high.
Knowledge graph. A database of entities and relationships. Search systems use knowledge graphs to understand that a business, founder, product, location, and category relate to each other.
Measurement terms
Prompt tracking. Running the same prompts across AI systems over time and recording which brands, sources, and answers appear. It is similar to rank tracking, but less stable.
Share of Model. An emerging metric for how often a brand appears across a set of AI prompts. If your brand appears in 12 of 50 tracked answers and a competitor appears in 30, that gap may guide content and reputation work.
Citation visibility. How often your URLs appear as cited sources in AI answers. Bing has started reporting this through AI Performance in Bing Webmaster Tools, where available.
Brand representation. The accuracy and favorability of how AI systems describe a brand. This is part visibility, part reputation, part data hygiene.
AI referral traffic. Visits that arrive from AI tools. Analytics may show referrals from platforms such as Perplexity, ChatGPT, Copilot, or others, depending on how the visit is passed through.
Files, crawlers, and access
AI crawler. A bot used by an AI company or search product to discover, retrieve, or train on web content. Different crawlers have different names and policies, so make decisions per crawler rather than blocking every unfamiliar user-agent.
robots.txt. A text file that tells crawlers which parts of a site they can access. Blocking a crawler may also remove the site from that crawler’s citations or retrieval results.
llms.txt. A proposed Markdown file that lists important pages for AI systems. It is not a universal standard, and Google says it doesn’t use llms.txt for Google Search or generative search features.
Sitemap. A file that lists canonical URLs for search engines. Sitemaps are still more established than llms.txt and should be handled first.
Structured data. Machine-readable markup, usually JSON-LD, that labels page content with schema.org types such as Article, Organization, Product, FAQPage, LocalBusiness, or BreadcrumbList.
How these terms fit together
Think of the work in layers.
First, SEO makes sure the page can be found and understood. Then AEO makes the answer easy to extract. GEO makes the page stronger as a source for generated answers. LLMO makes the brand or entity clearer. SXO makes the visit useful once someone arrives.
AI search optimization is the bucket that can contain all of those layers.
Terms to treat carefully
AIO. Some people use AIO to mean AI Optimization. Others mean Artificial Intelligence Optimization or AI Overview optimization. Ask for the definition before agreeing to the scope.
Guaranteed AI citations. Treat this as a red flag. AI answers vary by model, prompt, date, location, and retrieval source. You can improve the odds; you can’t guarantee every answer.
AI-first content. If this means clear answers and better source quality, fine. If it means mass-producing pages for bots, no. Pages still need to help people.
llms.txt strategy. An llms.txt file may be a small technical add. It should not be the main strategy while your sitemap, schema, content, links, or business facts are weak.
Practical next step
Take one page you care about and label it across the layers:
- SEO: can it be crawled, indexed, and ranked?
- AEO: does it answer the main question directly?
- GEO: does it include evidence, examples, and clear source quality?
- LLMO: does it describe your business, product, or topic accurately?
- SXO: does the visitor know what to do next?
Any blank answer is a better next task than memorizing another acronym.
FAQ
Which term should I use with a client or team? Use AI search optimization for the broad category, then define the specific work: AEO for answer formatting, GEO for citations and generated answers, LLMO for entity accuracy, SXO for the post-click experience.
Are these official Google terms? SEO is established. Some newer labels, such as GEO and LLMO, come from research, agencies, and tool vendors rather than Google product documentation. Use them as planning terms, not proof of a formal ranking system.
Should every business track AI prompts? Not every business needs a large prompt-tracking program. Most should start with five to ten buyer questions, checked monthly, then expand if AI answers clearly affect discovery.
What matters more, AI citations or website clicks? Both can matter. A citation may build trust or send traffic, while a click lets the visitor read, compare, contact, or buy. Track the outcome that maps to the business goal.