18 Essential AI-Search Terms: GEO, LLMs and More

As someone working with content and website optimisation on a daily basis, I have been coming across many different terms relating to GEO & AI-Search. It can honestly get quite confusing, as so many of them sound similar and mean almost the same thing.

So in this blog post, I want to go over a few AI-related keywords and clear up the definitions. The first part groups the terms into functions and explains them in more detail. In the second part, there is a quick glossary.

Key AI Search Terms Grouped by Function

A helpful way to understand all the various AI-related keywords is to group them according to function. Each area plays a distinct role in how AI works and how content is created, selected, and delivered.

1. Models → creation of content

These are the models that generate outputs when run within a system.

• AI models: trained systems that learn patterns from data to make predictions or decisions based on new inputs. Some AI models also generate new content - these are known as generative models.

• Generative models: AI models that can create new content (such as text, images, audio, or video) by learning patterns from existing data.

• LLMs (Large Language Models): a type of generative model focusing on text. GPT-5.3, Claude Opus 4.6, Gemini 3.1 Pro, LLaMA 4, and Mistral Large 3 are examples of LLMs.

• Image, audio, and video generative models also exist. For example, GPT Image and Midjourney generate images, while Suno and Udio generate audio.

These models are the foundation. Everything else builds on top of them.

2. Systems and engines → run and organise models

This terms refer to ways models are turned into usable systems.

• AI platforms: tools and infrastructure used to build, run, and manage AI systems (e.g. OpenAI, Google Cloud AI). They provide the environment in which multiple AI systems can be created and operated, whereas an AI system is a specific implementation designed to perform a task.

• AI system: a specific setup that uses AI to perform a task. It typically combines one or more models (which may or may not be generative), along with data and supporting infrastructure, to produce a usable outcome. Examples:

  • A customer support chatbot→ LLM + company knowledge base + chat interface

  • Netflix recommendations→ ML model + user data + recommendation system

  • Fraud detection in banking→ ML model + transaction data + alert system

• Generative engines: a subset of AI systems that uses generative models, along with data and system logic, to produce answers for users. Examples include ChatGPT, Perplexity AI, Microsoft Copilot, and Google Gemini.

This is what allows AI to produce structured, useful responses rather than raw predictions.

3. Interfaces and experiences → deliver outputs to users

These terms are related to ways users interact with AI.

• AI interfaces: this term refers to how you interact with AI - it can be chat, search, voice, or embedded tools

• AI search: any type of interface that uses AI to improve search

• Generative search: a subset of AI search that generates direct answers rather than just ranking links

• Generative search engines: a type of generative engine designed specifically to search, e.g. Perplexity AI, Google AI Overviews, Bing Copilot

• AI Overviews: summarised answers shown in search results

• AI Mode: settings where AI-generated responses are prioritised

• AI assistants: a type of AI interface focused on completing tasks conversationally.

The interface shapes how information is presented and experienced.

4. Optimisation → influence what gets selected

These terms are related to whether and how content is used.

• Generative Engine Optimisation (GEO): improves whether content is found and included

• LLM optimisation: improves how well content is understood and used

In practice, both are needed: GEO improves selection, whereas LLM optimisation improves interpretation. Together, these address both visibility and comprehension.

5. Outputs and key concepts → what users actually see

These terms relate to the final layer, where results are delivered.

• AI-generated content: content created by AI systems. This refers to the final output users see, rather than the model capability itself.

• Citation visibility: refers to how clearly and prominently a source is displayed within an AI-generated response, not just how often it is cited.

• Grounding answers: making sure that responses are supported by and linked to reliable sources or data, making them more verifiable and trustworthy. Grounding is typically achieved using retrieval-augmented generation (RAG), which connects AI models to external data sources.

These factors affect trust, accuracy, and attribution.

In simple terms

Models create content → Systems run it → Interfaces deliver it → Optimisation influences it → Outputs are what the user sees

Quick Glossary

Term	Refers To
AI Model	A trained system that uses patterns from data to make predictions or decisions from inputs (not all AI models generate new content).
Generative Model	An AI model that learns patterns from data and can generate new content (e.g. GPT-5.3, LLaMA 4, Gemini 3.1 Pro)
LLMs	Large language models are a type of generative model that generates and understands text based on patterns learned from data
Generative Engine	A broad term for any AI system that uses generative models, along with data and system logic, to produce answers for users
AI Platforms	Broadly refers to tools and infrastructure that host and deliver AI capabilities (tools, models, infrastructure)
AI Systems	Complete AI setups including models, data, and infrastructure working together for a specific function
AI Interfaces	The user-facing ways people interact with AI (chat, search, voice, embedded tools)
AI Assistants	AI tools designed to help users complete tasks via conversation or commands
AI Search	Any search system that uses AI at any stage
Generative Search	A type of AI search that creates a direct answer using a generative model
Generative Search Engine	A specific platform that combines a generative model with real-time web retrieval
AI Overviews	AI-generated summaries shown at the top of some search results (e.g. Google)
AI Mode	A search or app setting where responses are primarily generated by AI rather than traditional results
Generative Engine Optimisation (GEO)	The practice of structuring and optimizing online content so generative engines can easily retrieve, interpret, and surface it across AI interfaces.
LLM Optimisation vs. GEO	LLM focuses on improving how AI understands content; GEO focuses on whether that content gets selected and surfaced
AI-Generated Content	Content created by AI rather than directly written by a human
Citation visibility	How clearly and prominently sources are shown within an AI-generated response
Grounding an answer	The process of linking a generated response to reliable sources or data to ensure accuracy and verifiability