AI SEO glossary: 50 terms every marketer should know
Understanding the terminology is key to mastering modern SEO. This comprehensive glossary covers essential AI SEO concepts, performance metrics, and technical terms to help you navigate the evolving search landscape.
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AI Technology
Natural Language Processing (NLP)
A branch of AI that enables computers to understand, interpret, and generate human language, now heavily used in search algorithms.
AI Content Detection
Algorithms that identify content created by AI tools, potentially influencing how search engines evaluate content quality.
Content Quality
E-E-A-T
Experience, Expertise, Authoritativeness, and Trustworthiness - Google's framework for evaluating content quality and credibility.
Content Strategy
Content Clustering
A content organization strategy that groups related content around a pillar topic to establish topical authority and improve SEO performance.
Semantic SEO
An approach that focuses on meaning and relevance rather than just keywords, aiming to satisfy user intent through comprehensive content.
Topic Modeling
A technique that identifies patterns and themes in content to determine relevance to specific topics and queries.
Performance
Core Web Vitals
Google's performance metrics that measure page experience, including Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS).
LCP (Largest Contentful Paint)
A Core Web Vital that measures loading performance by timing how long it takes for the largest content element to become visible in the viewport.
FID (First Input Delay)
A Core Web Vital that measures interactivity by timing how long it takes for a page to respond to a user's first interaction.
CLS (Cumulative Layout Shift)
A Core Web Vital that measures visual stability by quantifying how much page elements move unexpectedly during page load.
Search Algorithms
Semantic Search
The process search engines use to understand user intent beyond keywords by analyzing context, meaning, and relationships between words.
BERT (Bidirectional Encoder Representations from Transformers)
A Google algorithm update using neural network-based techniques to better understand context in search queries.
MUM (Multitask Unified Model)
Google's advanced AI model that understands and connects information across languages and formats to deliver more comprehensive search results.
Entity SEO
Strategy focused on optimizing for entities (people, places, things) rather than just keywords, leveraging the Knowledge Graph.
Knowledge Graph
Google's database of entities and their relationships, used to enhance search results with contextual information.
Intent Analysis
The process of identifying and categorizing user search intent (informational, navigational, transactional, or commercial).
Neural Matching
Google's AI system that helps understand concepts behind content, even when specific keywords aren't used.
RankBrain
Google's machine learning algorithm component that helps process and interpret new or ambiguous queries.
Search Results
Featured Snippets
Selected search results displayed at the top of Google's results, designed to directly answer a user's query.
Technical SEO
Schema Markup
Structured data vocabulary that helps search engines understand website content and context, enabling rich results in search listings.