Your comprehensive guide to AI optimization terminology, from GEO and AEO to AI citations and beyond
A strategic approach to becoming a reliable, frequently-cited source in AI-generated responses. Involves building topical authority, consistency across platforms, and verifiable proof of expertise.
The practice of optimizing digital content and brand presence for AI-powered search, discovery, and recommendation systems. AIO encompasses GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), and AI Citation Strategy.
The practice of structuring content to directly answer user questions in a way that AI systems can extract and present as authoritative responses. AEO emphasizes clear, concise answers to common questions in your domain.
A methodology for optimizing content to be discovered, understood, and cited by generative AI platforms like ChatGPT, Claude, Perplexity, and Gemini. GEO focuses on entity clarity, structured data, and content that AI systems can confidently reference.
A metric that measures how frequently and prominently a brand appears in AI-generated responses across multiple platforms. Higher scores indicate better visibility in AI-powered search and discovery.
The percentage of relevant queries where your brand is cited or mentioned by AI platforms. Higher citation rates indicate stronger AI visibility.
Your brand's proportion of total citations or mentions compared to competitors within AI-generated responses for your industry or category.
AMPD's proprietary method of calculating AI visibility by analyzing multiple signals including citations, context, prominence, sentiment, and consistency across AI platforms.
Experience, Expertise, Authoritativeness, and Trustworthiness. Google's quality framework that's also relevant for AI systems evaluating content credibility.
The practice of crafting effective prompts or queries to get desired responses from AI systems. Understanding how users prompt AI helps optimize content for common query patterns.
The perceived expertise and reliability of a source on a specific topic, as determined by AI systems. Built through consistent, high-quality content and citations from trusted sources.
Indicators that help AI systems determine the credibility and reliability of information sources. Include citations, awards, credentials, case studies, and third-party validation.
The process of clearly defining what your business is, what it does, who it serves, and why it's credible—in a way that AI systems can easily understand and reference. Uses structured data, consistent naming, and clear categorization.
Structured data vocabulary that helps search engines and AI systems understand the content and context of web pages. Critical for entity optimization and AI visibility.
A database of entities (people, places, things, concepts) and their relationships. AI systems use knowledge graphs to understand context and provide accurate information.
AI systems trained on vast amounts of text data that can generate human-like responses, answer questions, and provide recommendations. Examples include GPT-4, Claude, Gemini, and Llama.
A technique where AI systems retrieve relevant information from external sources before generating responses. Understanding RAG helps optimize content for AI retrieval and citation.
Search that understands the intent and contextual meaning of queries rather than just matching keywords. AI-powered search is inherently semantic.
Commerce facilitated by AI agents that research, recommend, and sometimes purchase products/services on behalf of users. Represents the future of AI-driven discovery and transactions.
Queries where users get answers directly from AI or search systems without clicking through to websites. Emphasizes importance of being cited or referenced in the response itself.
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