Core AI Terminology Marketers need to learn
You’re probably hearing a lot of AI jargon right now – some familiar, some completely new. To help cut through the noise, we’ve pulled together some of the most common AI terminology, based on our own experience and research, to help you navigate how AI is shaping modern marketing.
AI – Artificial Intelligence
Example: “You need to understand how AI interprets your brand narrative.”
Definition: The common term used for technology that performs tasks such as understanding language, recognising patterns and generating content. ChatGPT, Claude and Gemini are all examples of AI models people are using.
LLM – Large Language Model
Example: “Different LLMs weight sources differently, so visibility may vary across platforms.”
Definition: A Large Language Model (LLM) is a type of AI powered model behind these AI assistants. Tools such as ChatGPT, Claude, and Gemini use Large Language capabilities to learn patterns from large historical data sets to generate their responses. These tools are often weighted slightly differently in how this information is retrieved with some assistants also using search tools to access information from the web to help them validate and generate better responses.
AI Search
Example: “We’ll optimise your content so it tracks in AI search, not just Google.”
Definition: Using an AI-powered tool to find information through questions or requests.
Prompts
Example: “Your prompt is too broad to be useful to this research.”
Definition: The questions or instructions we give to an AI assistant/model to perform a task. A prompt can be as simple as a question or a detailed multi layered request to produce content/ or action.
Signals
Example: “The model isn’t picking up enough signals that link your brand to this topic.”
Definition: The input an LLM detects – such as the content you create – that shapes what it chooses to share in its answers.
Surfacing
Example: “Greater alignment on topics will help your content surface more consistently.”
Definition: The moment an LLM selects and presents an idea, source or organisation in its answers.
Influence
Example: “Right now, competitor narratives have more influence on the LLM’s responses.”
Definition: The degree to which an organisation’s signals shape what an LLM chooses to share with users.
AI Visibility
Example: “Your AI visibility is too low to be appearing in the top level summaries.”
Definition: The frequency and prominence of how often your company appears in AI-generated answers across the likes of ChatGPT, Gemini, and Claude.
Authoritative content
Example: “You need more authoritative content linked to your core topics.”
Definition: Content that AI models recognise as credible, expert and closely aligned with the core topic enough to cite it.
AI Citations
Example: “If you’re not appearing in the AI citations, you won’t be influencing the answer.”
Definition: When an AI system pulls information from a third‑party source such as a website or a published article, it will cite that source to support its answer. These citations show users exactly where the information came from and help the system confirm its accuracy. This is known as an AI citation.
SEO – Search Engine Optimisation
Example: “SEO still matters, but it doesn’t guarantee you’ll appear in an AI platform.”
Definition: The practice of improving your website’s visibility in search engines. It helps potential customers find your business when searching for relevant products, services or information.
GEO - Generative Engine Optimisation
Example: “You need a GEO strategy to ensure ChatGPT actually includes you in supplier lists.”
Definition: Generative Engine Optimisation is the practice of optimising content to be discovered, cited, and recommended by AI systems like ChatGPT, Claude, Perplexity, and Google's AI Overviews. Unlike traditional SEO, which focuses more on ranking in traditional search engine results pages, GEO ensures your content is surfaced when AI models answer questions, generate summaries, or provide recommendations.
GEO focuses on making your content easily discoverable and citable by large language models through structured information, authoritative sourcing, and clear formatting that AI systems can reference.
AEO - Answer Engine Optimisation
Example: “Your competitors are investing in AEO to influence how AI tools highlight their solutions.”
Definition: Answer Engine Optimisation is the practice of organising and presenting content so that search engines and AI assistants can identify and use it to answer users’ questions directly. It focuses on clear, relevant answers that may appear in featured snippets, voice responses or AI-generated answers.
AEO and GEO
Example: “We should treat AEO and GEO as part of the same visibility strategy.”
Definition: AEO and GEO often overlap, and different industries use the terms in different ways. AEO is about making your content the best possible answer to a specific question. GEO is about ensuring your content is used, cited, or your company is recommended within AI‑generated responses for key related topics.
SOV in AI – Share of Voice
Example: “Our SoV in AI for this topic is tiny… we’re barely part of the conversation!”
Definition: Share of Voice in AI is how often your company appears in AI‑generated responses compared with competitors.
AVV - AI Visibility Value
Example: “Our AVV improved after we published that explainer. The model now ranks us higher.”
Definition: It’s a measure of how valuable the coverage is in terms of AI influence.
How they all fit together:
AI is essentially the broad technology. LLMs power language generation; SEO, GEO and AEO describe the overlapping approaches to improving how people discover your company and its information across search engines and AI-generated answers. AI visibility is about understanding how your organisation is being interpreted, surfaced and influenced within these AI systems and how your content impacts visibility.
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