An embedding is a representation of a piece of content (text, image, sound) as a vector, a list of several hundred or thousand numbers, computed by a model so that content with similar meaning gets similar vectors.
In practice, “employment contract” and “permanent job” will have neighboring vectors even if they share no words. This closeness is measured by computing a distance between vectors, which makes it possible to search by meaning rather than by keywords.
Embeddings are used for semantic search, recommendation, document clustering, duplicate detection and above all RAG: they find the passages in a document base most relevant to a question before passing them to the LLM.
They are produced by specialized models, offered by AI providers or available as open source, and stored in vector databases (Qdrant, Weaviate, Pinecone, pgvector for PostgreSQL…).
Why it matters when hiring
Plugging in an embeddings API is easy. Real depth shows in search quality: choosing the model for the language and domain, chunking documents, hybrid search with keywords, and above all measuring relevance on a test set. In an interview, a strong AI Engineer explains how they evaluated and improved their search, with their own figures to back it up.