Deep Learning

Embeddings & Representation Learning

An embedding maps an item into a dense vector so useful relationships become geometric. Text, images, users, products, code, and documents can all be represented this way.

Embeddings turn sparse symbols into dense vectors
The training objective defines similarity
Vector distance is not semantic truth
Embeddings enable retrieval at scale
Representation learning reduces manual feature work
Embedding systems need evaluation and governance

Embedding retrieval

The same model should usually embed queries and documents into the same space.

Semantic search shape

query_vec = embed(query)
ids = vector_index.search(query_vec, k=20)
candidates = load_documents(ids)
filtered = apply_permissions_and_freshness(candidates)
ranked = rerank(query, filtered)

Vector search is one stage in a retrieval pipeline, not the whole product.