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Glossary
Embedding
An embedding is a numerical vector representation of an item — such as a text passage, image, or user profile — produced by an embedding model so that items can be compared with a chosen similarity metric. In that learned space, geometric proximity often correlates with relatedness for the training domain and objective — but it does not universally guarantee “semantic similarity” in the everyday sense.
Why it matters
Embeddings are the bridge between unstructured content and machine-usable similarity search. Most contemporary semantic-search and RAG pipelines embed documents and queries, then retrieve nearest neighbors. Results depend on the embedding model, training data, chunking, domain fit, and the similarity metric (for example cosine similarity) — not on geometry alone as a universal meaning oracle.
Embeddings are not a database and not a search product. They are the representation layer. Operators should evaluate whether neighbors that look close in vector space are actually relevant for the business task.
Example (enterprise): A support organization embeds policy articles and past tickets. A new ticket’s embedding often retrieves related resolutions even when wording differs (“can’t log in” vs “authentication failure”), which keyword search alone would miss — provided the embedding model fits that support corpus.
Mechanisms
- Vector encoding — an embedding model maps inputs to vectors under a training objective and representation choice.
- Similarity metrics — distance or similarity functions rank candidates; metric choice affects which items appear “close.”
- Domain fit — embeddings improve when the model and corpus match the task (legal, clinical, product taxonomy); off-domain models can place false neighbors nearby.
Boundaries
- Embedding ≠ vector database. The database stores and indexes vectors; the embedding is the representation.
- Embedding ≠ semantic search as a full system. Semantic search is the retrieval objective; dense embedding retrieval is one common technique among others.
- High similarity scores are not proof of factual correctness or true human semantic equivalence; they measure proximity in a particular model’s space.
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