AI Search Glossary
Learn generative engine optimization terms and concepts
Along with AI Search comes a slew of new terms and concepts that are important to learn in order to optimize your web presence. 62 terms and counting — did I miss anything?
AI Search Landscape
The engines, answer surfaces, and shifts reshaping how people find information.
AEO (Answer Engine Optimization)
Optimizing content to be the direct answer that answer engines and AI assistants return. Closely related to GEO and often used interchangeably.
AI Answer Box
A block of AI-generated text at the top of results (like Google AI Answers or Perplexity summaries).
AI Overviews
Google's AI-generated summaries that appear at the top of search results, synthesizing information from multiple sources with links. Formerly part of Search Generative Experience (SGE).
Answer Engine
A system that returns a direct, synthesized answer to a query — like ChatGPT, Perplexity, or Google AI Overviews — instead of a list of blue links.
Conversational Search
Searching through a back-and-forth dialogue with an AI assistant, where each follow-up builds on prior context instead of starting a new keyword query.
Generative Engine
An AI system that generates original responses from a prompt, such as ChatGPT or Gemini — the engine that GEO optimizes for.
GEO
Generative Engine Optimization. The act of optimizing your web presence for AI search.
Perplexity
A generative engine and research tool that leverages live web access combined with large language models to answer user queries while including citations for the sources referenced.
Real-Time Retrieval Optimization
Optimizing for LLMs that pull live data (e.g., Perplexity, ChatGPT with browsing).
Zero-Click Search
A term coined by Rand Fishkin describing "any online experience where the user is encouraged to stay on a platform and discouraged from clicking away to another website."
GEO Strategy & Tactics
The plays for getting your brand surfaced and recommended by AI.
AI Share of Voice
How often your brand appears or is cited in AI answers for a topic, measured relative to your competitors.
AI Summary Optimization
The practice of structuring content so it's selected and summarized clearly by LLMs.
Anchoring Phrase
A clear, repeated phrase used to increase LLM recognition and semantic clarity.
Auto-discovery
LLMs finding and referencing content without needing structured prompts or explicit links.
Behavioral Anchoring
The process of making your brand "stick" in an LLM's knowledge and summarization patterns.
Brand Embedding
The degree to which your brand is woven into an LLM's latent knowledge, so it is surfaced in relevant answers even without an explicit prompt or link.
Canonical Pattern
The content structure an AI expects for a certain type of query (e.g., definition → use case → CTA).
Citation Distribution
The process of establishing brand citations across multiple web properties to enhance a brand's visibility and presence in responses generated by large language models.
Content Chunking
Organizing information into distinct, easily navigable sections that both artificial intelligence systems and human readers can readily process and comprehend.
Deep Embedding
The positioning of your brand or info in latent space, beyond superficial keyword matches.
Deflection Optimization
Designing answers that acknowledge but redirect queries to your preferred CTA or topic.
Implied Expertise
Structuring content to suggest depth and authority even without overt credentials.
Intent Enrichment
The practice of designing content that satisfies deeper or secondary user intents surfaced by LLMs.
Modular Summarization
Formatting content so parts can be lifted independently and still make sense.
Multimodal Optimization
Preparing content (text, images, audio, video) for engines that process multiple formats.
Named Mention
Your brand or author being referenced directly by an LLM (even without a link).
Open-Ended Prompt Targeting
Creating content that answers non-specific or exploratory questions well.
Paraphrase Reference
The practice of having your content utilized as source material, even when it has been reworded or rephrased in AI-generated output.
Pre-Cog Marketing
Pre-Cog Marketing is a strategy focused on addressing consumer needs before they've fully recognized or actively sought solutions. It leverages artificial intelligence's capability to recommend relevant solutions to users who aren't necessarily searching for them. The approach works by using chat history and contextual information. For instance, if someone mentions struggling with motivation, an AI system might suggest dietary changes, lifestyle adjustments, or relevant products based on what it knows about that individual. This marketing methodology differs fundamentally from conventional search optimization. Rather than optimizing for explicit user queries, pre-cog marketing emphasizes creating and distributing content that would support an AI system's decision-making process when making recommendations under particular circumstances. Essentially, it targets "query-less searches" by positioning content where AI systems will encounter it when recommending solutions. The term was developed by Chris Bolton and references language from Philip K. Dick's science fiction novel The Minority Report.
Semantic Dominance
Owning a concept space so thoroughly that AI tools pull from you by default.
Retrieval, RAG & Embeddings
How AI systems find and pull the content they use to answer.
Cosine Similarity
A math measure of how close two embeddings are in vector space, used to match a query to the most semantically relevant content.
Data Ingestion
How LLMs consume and encode content (via training, retrieval plugins, or live crawl).
Embedding
A numerical representation of text (or images) as a vector, letting models compare meaning mathematically rather than by exact words.
Grounding
Tying an AI's response to verifiable source material (retrieved documents and citations) to reduce hallucination and improve accuracy.
Latent Space
The high-dimensional map of meaning inside a model, where related concepts sit near one another and embeddings live.
Query Fan-out
A technique used by Google's AI Mode that expands one query into many related sub-queries run in parallel, then synthesizes them into a single answer. Being relevant to the sub-queries raises your odds of being cited.
Retrieval
The step where an AI system searches a knowledge source for relevant documents before generating an answer — the R in RAG.
Retrieval-Augmented Generation (RAG)
A technique where an LLM fetches relevant documents before generating an answer.
Semantic Search
Search that matches on meaning and intent using embeddings, rather than exact keyword matching.
Vector Database
A database that stores embeddings and quickly retrieves the most semantically similar items — the backbone of RAG and semantic search.
LLM Fundamentals
Core concepts behind the language models doing the work.
Context Window
The limit of tokens an LLM can "see" or remember during one input/output cycle.
Fine-tuning
Further training a base language model on specialized data to adapt its tone, behavior, or domain knowledge.
Inference
The process of a trained model generating output from an input prompt, as opposed to the training that built it.
Large Language Model (LLM)
An AI model trained on vast amounts of text to understand and generate human-like language — the technology behind ChatGPT, Claude, and Gemini.
Prompt
The input or instruction a user gives an AI model to produce a response.
Prompt Engineering
Crafting and refining prompts to get more accurate, useful, or consistent outputs from an AI model.
System Prompt
The foundational, usually hidden instructions that set an AI assistant's role, rules, and behavior before any user message.
Temperature
A setting that controls how random or deterministic a model's output is — lower is more focused, higher is more creative.
Token
The chunks of text (words or word pieces) that large language models use to process input and generate responses.
Structured Data & Technical
The markup and files that make your content machine-readable.
JSON-LD
The preferred format for adding structured data to web pages; Google recommends it for Schema.
llms.txt
A proposed plain-text file at a site's root that gives LLMs a curated, easy-to-parse guide to its most important content — an AI-era companion to robots.txt and sitemaps.
Rich Snippets
An established SEO concept referring to enhanced search results that display supplementary information such as star ratings, frequently asked questions, and pricing details. These enriched results are generated through the implementation of structured data markup.
Schema Depth
The completeness and clarity of your structured data as readable by generative engines.
Schema Markup
Structured data added to your site (usually in JSON-LD) to help search engines and LLMs understand your content.