Glossary

AI & LLM SEO Terms

56 cutting-edge terms for the AI search revolution, including LLMs, SGE, semantic search, vector embeddings, and generative engine optimization.

This glossary covers the rapidly evolving landscape of AI-powered search. Terms may evolve as technology advances.

AI Search Platforms Large Language Models Semantic Search & Understanding Vector Search & Embeddings RAG & Knowledge Systems AI Content & E-E-A-T Future Search Concepts

8AI Search Platforms

SGE (Search Generative Experience): Google's AI-powered search feature that generates conversational summaries at the top of search results. Uses generative AI to synthesize information from multiple sources, often reducing clicks to websites.
AI Overviews: Google's rebranded term for SGE. AI-generated summaries that appear at the top of search results, answering queries directly using information synthesized from multiple web sources.
Bing Copilot: Microsoft's AI-powered search assistant integrated into Bing. Powered by GPT-4, it provides conversational answers with cited sources and can perform complex tasks.
Perplexity AI: An AI-powered answer engine that combines conversational AI with web search. Known for citing sources transparently and providing comprehensive answers with minimal ads.
ChatGPT Search: OpenAI's web search capability integrated into ChatGPT. Allows the AI to browse the web for current information and cite sources in responses.
Claude: Anthropic's AI assistant known for nuanced understanding and lengthy context windows. Increasingly used for research queries and content analysis.
Google Gemini: Google's multimodal AI model family powering various Google products. Gemini Ultra, Pro, and Nano versions power different applications including search features.
You.com: An AI-powered search engine offering personalized results, app integrations, and an AI chat interface. Emphasizes user privacy and customization.

9Large Language Models

LLM (Large Language Model): AI models trained on massive text datasets that can understand and generate human-like text. Examples include GPT-4, Claude, Gemini, and LLaMA. The foundation of AI search.
GPT (Generative Pre-trained Transformer): OpenAI's family of large language models. GPT-4 powers ChatGPT Plus and many AI applications. Uses transformer architecture for text generation.
Transformer Architecture: The neural network architecture underlying modern LLMs. Uses attention mechanisms to process sequences of text, enabling understanding of context and relationships.
Foundation Model: Large AI models trained on broad data that can be adapted for various tasks. Examples include GPT-4, Claude, and Gemini. Serve as the basis for specialized applications.
Multimodal AI: AI models that can process and generate multiple types of content—text, images, audio, and video. Google Gemini and GPT-4V are multimodal models.
Context Window: The maximum amount of text an LLM can process in a single interaction. Larger context windows allow for more comprehensive understanding. Claude offers 100K+ tokens.
Token: The basic unit of text processing for LLMs. Roughly 4 characters or 0.75 words in English. Models have limits on input and output tokens.
Fine-tuning: Customizing a pre-trained LLM on specific data or tasks to improve performance for particular use cases. Used to create specialized AI applications.
Prompt Engineering: The practice of crafting effective prompts to get desired outputs from LLMs. Critical skill for optimizing AI interactions and content generation.

8Semantic Search & Understanding

Semantic Search: Search that understands the meaning and intent behind queries rather than just matching keywords. Uses NLP and machine learning to deliver more relevant results.
Natural Language Processing (NLP): AI technology that enables computers to understand, interpret, and generate human language. Powers semantic search, content analysis, and AI assistants.
Natural Language Understanding (NLU): A subset of NLP focused on machine comprehension of text meaning, intent, and context. Enables search engines to understand complex queries.
Entity Recognition: AI's ability to identify and classify named entities (people, places, organizations, concepts) in text. Helps search engines build knowledge graphs.
Intent Classification: AI's ability to determine the purpose behind a search query (informational, navigational, transactional, commercial). Critical for delivering relevant results.
Sentiment Analysis: AI's ability to determine emotional tone in text (positive, negative, neutral). Used in brand monitoring and review analysis.
Topic Modeling: AI technique for discovering abstract topics in document collections. Helps understand content themes and topical relationships.
Co-occurrence Analysis: Analyzing which terms frequently appear together to understand semantic relationships. Informs keyword research and content optimization.

7Vector Search & Embeddings

Vector Embedding: Mathematical representations of words, sentences, or documents as numerical vectors. Enables AI to understand semantic similarity and relationships between content.
Vector Database: Databases optimized for storing and querying vector embeddings. Examples include Pinecone, Weaviate, and Milvus. Power semantic search applications.
Vector Search: Finding similar items by comparing their vector embeddings rather than exact keyword matches. Enables semantic search and recommendation systems.
Cosine Similarity: A mathematical measure of similarity between two vectors. Used to compare content embeddings and determine semantic relatedness.
Nearest Neighbor Search: Algorithm for finding the most similar vectors to a query vector. The basis for semantic search and recommendation systems.
Embedding Model: AI models that convert text into vector representations. Examples include OpenAI's Ada, Google's PaLM embeddings, and open-source alternatives like BERT.
Semantic Similarity: The degree to which two pieces of content have similar meanings, measured through vector comparison rather than keyword matching.

8RAG & Knowledge Systems

RAG (Retrieval-Augmented Generation): A technique combining information retrieval with LLM generation. The AI retrieves relevant documents, then generates answers based on that specific information. Reduces hallucinations.
Knowledge Graph: A structured database of entities and their relationships. Google's Knowledge Graph powers knowledge panels and helps understand entity relationships.
Entity: A distinct, well-defined concept (person, place, thing, concept) that can be identified and described. Entities are nodes in knowledge graphs.
Grounding: Connecting LLM outputs to verifiable information sources. RAG is a form of grounding that reduces AI hallucinations by referencing real content.
Hallucination: When an AI generates plausible-sounding but factually incorrect information. A major challenge in AI search that RAG and citations help address.
Citation: References to source material in AI-generated content. Increasingly important as AI search platforms attribute information to original sources.
Source Attribution: The practice of identifying where AI-generated information originated. Critical for trust and for websites to receive credit in AI search.
Knowledge Cutoff: The date through which an AI model was trained. Information after this date may not be known to the model without real-time search.

8AI Content & E-E-A-T

E-E-A-T: Experience, Expertise, Authoritativeness, Trustworthiness—Google's quality guidelines for content evaluation. More important than ever in the AI era as authenticity matters.
AI-Generated Content: Content created by artificial intelligence. Google allows AI content if it's helpful and high-quality. Pure AI content without human oversight often underperforms.
AI Content Detection: Tools and techniques for identifying content written by AI. Accuracy is imperfect, and Google focuses on quality rather than origin.
Human-in-the-Loop: AI systems that involve human oversight and intervention. Recommended for content creation to ensure accuracy, originality, and expertise.
Content Authenticity: Proof that content is original and created by who claims to have created it. Becoming important as AI-generated content proliferates.
Author Authority: The credibility and expertise of content creators. More important in AI search as engines seek to cite authoritative human sources.
Information Gain: The unique value content provides beyond what's already available. AI-generated content often lacks information gain, hurting rankings.
Helpful Content: Google's standard for quality content—created for people, not search engines, and providing genuine value. Central to surviving AI search changes.

8Future Search Concepts

Zero-Click Search: Queries answered directly in search results without requiring a click to any website. AI overviews are accelerating zero-click trends.
Answer Engine: Search systems optimized to provide direct answers rather than lists of links. Represents the future direction of AI-powered search.
Conversational Search: Search interactions that mimic natural conversation, with follow-up questions and context retention. Enabled by AI chat interfaces.
Agentic AI: AI that can take autonomous actions to accomplish goals, not just generate text. Future search may involve AI agents performing tasks on behalf of users.
Personalized AI Search: AI search results tailored to individual users based on their history, preferences, and context. Raises privacy and filter bubble concerns.
Multimodal Search: Search using multiple input types—text, images, voice, video. Google Lens and voice assistants are early examples; AI enables richer multimodal experiences.
Generative Engine Optimization (GEO): Emerging term for optimizing content to appear in AI-generated search responses. Focus on being cited by AI rather than just ranking in traditional results.
AI Search Visibility: How often and prominently your content is cited or referenced in AI-generated search responses. A new metric beyond traditional rankings.