Embedding

An embedding represents text, images or other data as a numeric vector in a multidimensional space.

An embedding model is trained to encode selected content features so that similarity can be compared mathematically. The type of similarity captured depends on the model and its task.

How embeddings capture meaning

An embedding model converts a sentence or passage into a fixed-length vector. Semantically related wording can therefore be close together even when it uses different words.

Semantic search uses this property to find content by learned similarity rather than exact search terms alone.

What embeddings are actually used for

In RAG systems, embeddings support the retrieval of relevant document passages. The vectors are often stored in a vector database.

Other uses include detecting similar content, recommendation systems and grouping unstructured data.

Limits and practical pitfalls

Embeddings do not encode a universal form of meaning. Their quality depends on the model’s training, language, domain and specific task.

Individual vectors are not directly interpretable by people. Test retrieval or classification quality with realistic domain examples and monitor it after changes to the model or data.