Numerical representations of text, images, or other data that capture semantic meaning, enabling similarity search and machine learning.
Embeddings are numerical representations of text (or images) that capture meaning. Similar items have similar vectors, which enables semantic search, clustering, recommendations, and retrieval for AI systems. In practical terms, embeddings help you find “related” content even when exact keywords don’t match.
Embeddings are especially valuable for large content libraries (glossaries, knowledge bases, documentation) where traditional keyword search struggles.
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.
Fine-Tuning
Adapting a pre-trained AI model to a specific task or domain by training it on additional specialised data.
Large Language Model (LLM)
A type of AI model trained on vast text data to understand and generate human-like language, powering chatbots, content tools, and search features.
Related GEO services, audits, and frameworks that operationalise Embeddings in commercial execution.
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