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Vector Database
Vector databases power semantic retrieval by matching meaning, not only exact wording, across large multilingual datasets.
Definition
A specialised database designed to store and retrieve vector embeddings efficiently for similarity search and semantic retrieval.
How It Works
Vector Database helps teams build predictable AI and translation workflows by setting clear expectations for quality, consistency, and decision-making.
In production environments, this concept is applied with process controls such as human review, terminology alignment, and repeatable quality checks across multilingual content.
In RAG and translation workflows, retrieval quality and indexing efficiency directly shape output quality, speed, and user trust.
Key Concepts
- core principle of vector database
- workflow-level implementation
- terminology and quality consistency
- human validation before publication
Where It Is Used
- localisation workflows
- AI translation pipelines
- multilingual content production
- cross-referencing related concepts such as Validation Dataset