Techniques & Methods
Word Embedding
Word embeddings map words into continuous vector spaces where semantically similar words are placed near each other. Word2Vec, GloVe, and FastText were early landmark embedding methods; modern contextual embeddings from transformer models have largely superseded them.
Embeddings are foundational to nearly all NLP systems, enabling models to work with meaning rather than arbitrary word IDs. They power semantic search, recommendation systems, and the retrieval component of RAG architectures.
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Related Terms
Model Components
Embeddings
Dense numerical vectors that represent text, images, or other content in a high-dimensional space where semantically similar items are geometrically close — the foundational data structure for semantic search and RAG retrieval.
Techniques & Methods
Semantic Similarity
Measure of how closely related two pieces of text are in meaning.
Techniques & Methods
Semantic Search
Search technology that retrieves results based on the meaning of a query rather than exact keyword matches — using embeddings to represent queries and documents as vectors and finding nearest neighbors in semantic space.
Techniques & Methods
Vector Representation
Encoding words, sentences, or concepts as numerical vectors for AI comparison and retrieval.