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Technical implementation · AI Search Infrastructure

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Definition

Word embedding is a technique for representing words as numerical vectors in a high-dimensional space, where words with similar meanings are positioned close together. Word embeddings are the foundational technology underlying semantic search and modern language models. Word embeddings are why semantic relevance works the way it does. When an AI system retrieves content related to a query, it is computing the similarity between the query’s embedding and the embeddings of candidate documents — not counting keyword matches. Content that is semantically rich, uses related concepts and entities, and covers a topic comprehensively produces better embeddings than content optimized for keyword frequency alone.

Embedding

Semantic search

Vector database

Neural matching

Semantic relevance

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