Choosing the Right Embedding Dimension for Semantic Search

Executive Summary: Embedding dimension - the length of the vector used to represent text, images or other data - is a critical hyperparameter for semantic search. Higher dimensions let embeddings capture finer-grained semantics, often boosting recall and precision, but come at the cost of greater latency, storage, and compute. Too few dimensions starve the model of capacity (causing underfitting), while too many invite noise or overfitting. In practice, most production search systems use moderate dimensions (e.g. 384-768) enough to cover typical content without excessive overhead.