An AEO glossary is a reference list of the terms used in answer engine optimization, the practice of making your content easy for AI systems to understand, retrieve and cite. Below are 40 terms in plain English, grouped by what you do with them, with notes on where terms get confused. Definitions are my own wording, informed by the glossaries linked at the end; where a source defines a term in a distinctive way, I say so.
As of 4 October 2026. This vocabulary moves fast, and vendors use some terms loosely. When a term is contested, I say so rather than pretend there is one official definition.
Key Takeaways
- AEO, GEO and LLMO overlap heavily. Conductor and others use them for closely related goals; there is no standards body, so define the term in your own reports.
- Google says there are no additional requirements to appear in AI Overviews or AI Mode beyond SEO fundamentals, per its AI features documentation.
- A mention (your name appears) is not a citation (your content is credited). Track them separately.
- Retrieval terms such as RAG, grounding and query fan-out explain why some pages get quoted.
- Agent terms (AI agent, MCP, computer use) are the newest layer, and they affect how tools reach your content and your customers.
- Use the quick-reference tables to align your team on one vocabulary before you measure anything.
How to use this glossary
Each group below is a table: term, definition, and why a marketer cares. The groups are strategy, Google's AI search features, retrieval and technical terms, measurement, and agents. At the end there are three "X vs Y" comparisons for the pairs people mix up most.
Group 1: Strategy and discipline terms
Answer engine optimization (AEO)
Answer engine optimization is optimizing content so AI engines can understand it and surface it as answers in AI Overviews, snippets and chat responses. Conductor's glossary frames it this way. I wrote more in my post on AEO evidence.
Generative engine optimization (GEO)
Generative engine optimization is optimizing so generative AI tools cite and name your content. Clariant Creative describes it as optimizing so tools cite your content as a source. In practice GEO and AEO are used almost interchangeably.
| # | Term | Plain-English definition | Why it matters |
|---|---|---|---|
| 1 | AEO | Optimizing content to be surfaced as the answer by AI and answer features | The umbrella goal for most teams |
| 2 | GEO | Optimizing to be cited inside generative AI answers | Often the same work, framed around citations |
| 3 | LLMO | Large language model optimization; the broadest label for how brands appear in AI recommendations | Seen in vendor material; very loosely defined |
| 4 | AIO (AI Optimization) | Optimizing for AI systems generally, sometimes meaning AI Overviews specifically | Ambiguous, so clarify |
| 5 | SEO | Search engine optimization for ranked results | Still the foundation Google points to |
| 6 | Agentic AEO | Using AI agents to monitor, decide and execute AEO tasks (Conductor's term) | Automation of the work itself |
Group 2: Google's AI search features
| # | Term | Plain-English definition | Why it matters |
|---|---|---|---|
| 7 | AI Overview | Google's AI-written summary at the top of some results, with links | Can reduce clicks but adds citation chances |
| 8 | AI Mode | A conversational mode in Google Search with follow-up questions | Longer, multi-step queries |
| 9 | Query fan-out | Breaking one query into several sub-searches across topics and sources | Why you can be cited for queries you never targeted |
| 10 | Zero-click search | A query that ends with no click because the answer is on the results page or in an AI response | Changes how you value impressions |
| 11 | Inline citation | A clickable link inside an AI answer pointing to a source | The visible prize in AI answers |
| 12 | Search Console AI reporting | Search Console data covering AI feature performance | Where Google's own numbers live |
Google's documentation says AI Overviews and AI Mode use query fan-out, "issuing multiple related searches across subtopics and data sources", which can show a wider and more diverse set of links. The page showed a last update of 10 December 2025.
Group 3: Retrieval and technical terms
| # | Term | Plain-English definition | Why it matters |
|---|---|---|---|
| 13 | LLM | Large language model; the AI system behind ChatGPT, Claude, Gemini and Copilot | The engine behind generative answers |
| 14 | RAG | Retrieval-augmented generation: the model fetches outside information before answering | Why live web pages can feed answers |
| 15 | Grounding | Anchoring a response to specific source material such as live search results | Enables citations (Searchable's framing) |
| 16 | Hallucination | A confident but false AI output | Why fact-checking matters |
| 17 | Vector embedding | A numerical representation of text capturing meaning | How semantic matching works |
| 18 | Vector search | Searching by meaning using embeddings, not keywords | Explains why synonyms still match |
| 19 | Semantic search | Search that interprets meaning and intent | Core of modern retrieval |
| 20 | Conversational search | Searching with natural-language, dialogue-style questions | Shapes how you write headings |
| 21 | Content chunking | Splitting a page into passages that can be retrieved and quoted alone | Write self-contained sections |
| 22 | Entity | A thing (brand, person, product) that systems can identify and relate to others | Brand clarity |
| 23 | Knowledge graph | A database of entities and their relationships | Backbone of entity understanding |
| 24 | Structured data (schema) | Code, usually schema.org JSON-LD, that labels what a page contains | Machine-readable meaning |
| 25 | DefinedTerm | A Schema.org type for a word or phrase with a formal definition | Good for glossaries; see below |
| 26 | E-E-A-T | Experience, expertise, authoritativeness, trust: the qualities raters use to judge content | Trust signals |
| 27 | Crawler (e.g. GPTBot) | A bot that fetches pages for training data or live answers | Controls access via robots rules |
| 28 | llms.txt | A proposed root-level markdown file summarising a site for language models, introduced by Jeremy Howard in September 2024 per Searchable | Proposed, not a confirmed ranking or citation signal |
On DefinedTerm: Schema.org describes it as "a word, name, acronym, phrase, etc. with a formal definition", commonly used for glossaries and dictionaries, with properties such as name, description and inDefinedTermSet. If you publish a glossary, it is the natural fit. Adding it will not guarantee citations; it just labels your content clearly.
On llms.txt: treat it as an experiment. I have not seen primary-source confirmation that major engines rely on it, so I would not spend a sprint on it.
Group 4: Measurement terms
| # | Term | Plain-English definition | Why it matters |
|---|---|---|---|
| 29 | AI citation | A visible credit linking an AI answer to your page | Counts toward citation rate |
| 30 | Brand mention | Your brand named in an AI answer, linked or not | Awareness signal |
| 31 | Citation rate | Answers citing your domain divided by total monitored answers, times 100 (Rankshift's formula) | Core KPI |
| 32 | AI share of voice | Your share of all brand mentions in tracked AI answers | Competitive view |
| 33 | Prompt tracking | Running a fixed set of buyer questions through AI engines on a schedule | Basis of any measurement |
| 34 | Prompt set | The fixed list of questions you track | Keep constant for trends |
| 35 | Answer volatility | How much answers change run to run | Why snapshots mislead |
Rankshift notes that two identical queries on different days can produce different citations, which is why trend beats snapshot. I cover the maths in my AI share of voice definition post.
Group 5: Agent and protocol terms
| # | Term | Plain-English definition | Why it matters |
|---|---|---|---|
| 36 | AI agent | Software that uses AI to take multi-step actions toward a goal | Buyers may delegate research and purchases |
| 37 | Agentic search | Search where agents run multi-step research across sources | Another route to your content |
| 38 | MCP | Model Context Protocol, an open standard for connecting AI applications to external data, tools and workflows | How agents plug into your tools |
| 39 | Computer use | An agent operating software through screenshots, mouse and keyboard | Agents can use websites like people |
| 40 | Scaled content abuse | Many pages made mainly to manipulate rankings, not help users (Google spam policy) | The risk of mass AI publishing |
The MCP documentation calls it an open-source standard for connecting AI applications to external systems and compares it to a USB-C port for AI. Computer use is real and documented: Anthropic's computer use tool docs describe giving Claude screenshot, mouse and keyboard control, and OpenAI said at DevDay that its Agents API now supports computer use, per its DevDay 2026 recap. I explain the agent idea in what is an AI agent for marketers.
On scaled content abuse, the exact wording is in Google's spam policies. I unpack it in my scaled content abuse definition post.
The three comparisons people ask for most
AEO vs GEO
| Aspect | AEO | GEO |
|---|---|---|
| Focus | Being the answer in snippets, overviews and assistants | Being cited in generative responses |
| Origin | Grew from featured-snippet and voice optimization | Framed around generative engines |
| Overlap | Very high | Very high |
| My practice | Use "AEO" externally, track citations internally | Same work |
Mention vs citation
| Aspect | Mention | Citation |
|---|---|---|
| What happens | Brand named | Content credited with link or attribution |
| Signals | Awareness | Authority and potential traffic |
| Reported by | Mention rate | Citation rate |
Similarweb's AI search team puts it as: mentions build awareness, citations build authority and drive traffic.
AI Overview vs AI Mode
| Aspect | AI Overview | AI Mode |
|---|---|---|
| Where | Summary on the results page | A conversational mode with follow-ups |
| Interaction | Mostly read-and-click | Multi-turn |
| Shared technique | Query fan-out | Query fan-out |
Terms to be cautious about
Some vocabulary is marketing, not science. "LLMO" and "AIO" are used inconsistently. Anything claiming a guaranteed "AI ranking factor" is vendor language; Google states there are no extra requirements for its AI features. Where I could not confirm that a practice (like llms.txt) is used by engines, I said so above.
Sources I drew on
Definitions here are my wording, informed by Conductor's glossary, Searchable's 2026 glossary and Clariant Creative's AEO glossary. Vendor glossaries have a commercial interest in their categories, so cross-check anything you plan to build strategy on.
FAQ
What does AEO stand for?
Answer engine optimization. It means making your content easy for AI engines and answer features to understand and present as an answer, as Conductor's glossary frames it.
Is GEO different from AEO?
Only slightly in framing. GEO emphasises being cited by generative tools; AEO emphasises being the answer. In daily work the tasks overlap heavily.
What is query fan-out?
It is the technique where one question is split into several related searches across subtopics and sources. Google's AI features documentation says AI Overviews and AI Mode use it.
What is the difference between a mention and a citation?
A mention is your name appearing in an answer. A citation credits your content with a link or attribution. Track both separately because they behave differently.
What is RAG in simple terms?
Retrieval-augmented generation lets a model fetch outside information before it answers, which helps accuracy and lets it point to sources.
Do I need llms.txt?
It is a proposal introduced by Jeremy Howard in September 2024, per Searchable. I have not found primary confirmation that major engines depend on it, so treat it as optional.
What is MCP?
Model Context Protocol is an open standard for connecting AI applications to external data sources, tools and workflows, according to its documentation.
Should I use DefinedTerm schema on a glossary?
It is a sensible fit, since Schema.org defines it for terms with formal definitions. It labels content clearly but does not guarantee citations.
Where should a beginner start?
Learn the measurement terms (citation, mention, share of voice, prompt set), then audit what AI engines say about you today before changing content.
Need help turning vocabulary into a plan?
If you want these terms translated into an audit and a content plan for your brand, see my work at younusfardeen.in and reach me through the contact form. I am an organic growth and AEO marketer with 4+ years of marketing experience across edtech and startup work.