Embedding

How embeddings are created

Dedicated embedding models (OpenAI text-embedding-3, Cohere Embed, Voyage AI, open-source models such as BGE-M3) convert an input text into a float vector. The model has been trained on vast text corpora to learn semantic similarity — sentences such as ”How fast does my website load?” and ”measuring page speed” end up with vectors that sit close together, even though they share no common word. Similarity is typically measured as cosine similarity or dot product.

Embeddings in AI search

The retrieval phase of modern AI search engines almost always runs on embeddings: (1) the user query is converted into a vector; (2) a vector database looks for the K nearest vectors (that is, the semantically most similar content); (3) this content is passed to the LLM as context. The effect for GEO: content written broadly and coherently around a topic has ”better” embeddings — it sits closer in the vector space to a variety of related queries. Narrow keyword-stuffed texts occupy a narrow position in the embedding space and are retrieved less often for thematically broad queries.

What this means for content strategy

• Write broadly rather than narrowly on a topic — related terms, synonyms, different formulations.
• Completeness of content — address every important aspect of the topic, not just the main keyword.
• Entity linking — name relevant named entities (people, places, concepts) explicitly.
• Variety of questions — cover various possible user queries in the text as H2/H3 headings.
• A clear topic profile — avoid off-topic sections that ”dilute” the embedding.