vector embedding optimization
noun

Definition:
The process of refining or influencing the way data—such as text, entities, or documents—is represented in vector space to improve semantic accuracy, clustering behavior, or retrieval performance within AI systems. Vector embedding optimization involves adjusting language, structure, context, and metadata so that the resulting embeddings better reflect the desired relationships and distinctions when processed by models using techniques like semantic search or retrieval-augmented generation (RAG).

Usage:
“Through vector embedding optimization, the team ensured their product pages clustered more closely with top-ranking competitors in semantic search.”

Compare:
Semantic Optimization, Retrieval Tuning, Generative Engine Optimization (GEO), Embedding Engineering

First Known Use:
2020s, as LLMs and vector-based search systems became central to modern AI workflows.