A database optimised for storing and querying high-dimensional vectors (embeddings), enabling semantic search and AI applications.
A vector database stores embeddings (vectors) and supports fast similarity search. Instead of matching keywords, vector search retrieves items that are semantically similar - useful for RAG, recommendations, clustering, and semantic search across large content libraries.
Browse related definitions in the same glossary category.
AI Grounding
Techniques to anchor AI outputs in verified sources and facts, reducing hallucinations and improving reliability.
AI Hallucination
A phenomenon where a large language model generates false or illogical information but presents it as fact.
Citation Confidence
A measure of how accurately an AI model attributes information to its original source.
Content Provenance (C2PA)
Technical standards for verifying the origin and authenticity of digital content, increasingly important for AI-generated media.
Embeddings
Numerical representations of text, images, or other data that capture semantic meaning, enabling similarity search and machine learning.
Fine-Tuning
Adapting a pre-trained AI model to a specific task or domain by training it on additional specialised data.
Related GEO services, audits, and frameworks that operationalise Vector Database in commercial execution.
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