Embeddings
Embeddings are numerical vector representations of text, used by AI systems to measure semantic similarity. They underpin retrieval in AI search and influence which sources get selected as relevant for a query.
What it is
An embedding is a high-dimensional vector that represents the meaning of a piece of text. Two pieces of text with similar meaning have vectors that are close together in vector space. AI engines use embeddings to find content semantically related to a user's query, even when the exact words don't match. Understanding embeddings explains why GEO is not about keyword matching — the AI doesn't need your page to contain the user's exact words; it needs your page to be semantically about the same thing.
Why it matters for GEO
GEO is semantic, not lexical. Pages that genuinely answer the underlying question get cited even if they don't repeat the user's keywords.
- Retrieval-Augmented Generation (RAG) — Retrieval-Augmented Generation (RAG) is an AI architecture where a model retrieves relevant external content at query time and uses it to generate an answer.
- Generative Engine Optimization (GEO) — Generative Engine Optimization (GEO) is the practice of structuring a brand's content, entity footprint, and third-party signals so that AI engines like ChatGPT, Perplexity, Claude, and Google AI Overviews cite that brand inside their generated answers.
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