MY ONE BLOGby Manishdai
Artificial Intelligence

What is Embedding?

Quick answer

A list of numbers (a vector) that represents a word, sentence, image, or other item so that similar items have similar vectors.

Embedding, explained

Computers cannot compare meaning directly, but they can compare numbers. An embedding model converts content into a vector, often with hundreds or thousands of dimensions, positioned so that related items sit close together. The vectors for puppy and dog end up near each other, while the vector for invoice is far away.

Embeddings power semantic search, recommendation engines, duplicate detection, clustering, and retrieval-augmented generation. For example, a blog could embed all its articles, embed a reader's search query, and return the posts whose vectors are most similar, even if they do not share exact keywords. Embeddings are usually stored and searched in a vector database built for fast similarity lookups.

Related terms

Natural Language Processing

A field of AI focused on enabling computers to understand, interpret, and generate human language.

Retrieval-Augmented Generation

A technique that improves AI answers by first retrieving relevant documents and then giving them to a language model as context.

Semantic Search

Search that finds results based on the meaning of a query rather than exact keyword matches.

Vector Database

A database designed to store embeddings and quickly find the items most similar to a given query vector.

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