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Vector Embedding

Embedding

An embedding is a list of numbers, called a vector, that represents the meaning of a piece of data such as a word, sentence, document or image. Items with similar meaning get vectors that sit close together, which lets software compare meaning mathematically instead of matching exact words.

Computers cannot compare meaning directly, but they can compare numbers. An embedding model reads a piece of content and outputs a vector with hundreds or thousands of dimensions. Two sentences that say the same thing in different words, such as how to lower ad costs and ways to reduce CPC, end up with similar vectors even though they share few words.

What Are Embeddings Used For?

  • Semantic search: finding results by meaning rather than exact keywords, the idea behind neural search.
  • RAG: finding the passages most relevant to a question so an LLM can answer from them.
  • Recommendations: suggesting similar products or articles.
  • Clustering and deduplication: grouping similar queries, reviews or pages.

How Are Embeddings Stored and Searched?

Each piece of content is embedded once and the vectors are stored, usually in a vector database. When a question comes in, it is embedded with the same model and the database returns the stored vectors closest to it. Closeness is typically measured with cosine similarity.

Why Embeddings Matter for SEO

Search engines and AI assistants use embeddings to understand what a page is about, not just which words it contains. That is one reason covering a topic clearly and completely, building topical authority, tends to beat repeating a keyword. A page that answers the real question in plain language is easier to match to the many ways people phrase it.

Frequently asked questions

What is an embedding in AI?+

An embedding is a vector, a list of numbers, that represents the meaning of text, images or other data so that similar items have similar numbers and can be compared by meaning.

What is the difference between an embedding and a token?+

A token is a piece of text a model reads or writes. An embedding is a numeric representation of meaning, for a token, sentence or whole document, used to compare similarity.

Where are embeddings stored?+

Usually in a vector database or a regular database with vector search support, which can quickly find the stored embeddings closest to a new query.

Related terms

Vector Database

Vector Database

A vector database is a database built to store embeddings, the numeric vectors that represent meaning, and to find the vectors most similar to a query very quickly. It is the storage and search layer behind most semantic search and RAG systems.

RAG

Retrieval-Augmented Generation

RAG (Retrieval-Augmented Generation) is a technique that lets a large language model retrieve relevant information from an external source before generating an answer, rather than relying only on what it learned during training. It grounds the model's response in specific, up-to-date documents.

Neural Search

Neural Search

Neural search is a search method that uses AI models to match results by meaning rather than by exact keywords. It converts text into vector embeddings, numerical representations of meaning, and returns results whose meaning is closest to the query, even when they share no words with it.