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AI Glossary

Embedding

A numerical vector representation of text that captures semantic meaning for AI processing.

TL;DR

  • —A numerical vector representation of text that captures semantic meaning for AI processing.
  • —Understanding Embedding is critical for effective AI for companies.
  • —Remova helps companies implement this technology safely.

In Depth

Embeddings convert text into dense numerical vectors that capture semantic relationships. Similar concepts produce similar vectors. Embeddings are used for semantic search, RAG document retrieval, content similarity analysis, and classification tasks. They're fundamental to modern AI knowledge base systems.

Related Terms

Vector Database

A database optimized for storing and querying high-dimensional vector embeddings for AI applications.

Retrieval-Augmented Generation (RAG)

A technique that grounds AI responses in retrieved documents to improve accuracy and reduce hallucinations.

Knowledge Graph

A structured representation of relationships between entities used to enhance AI reasoning.

Large Language Model (LLM)

A deep learning model trained on vast text datasets that can understand and generate human-like text.

Knowledge Hub

Glossary FAQs

Embedding is a fundamental concept in the AI for companies landscape because it directly impacts how organizations manage a numerical vector representation of text that captures semantic meaning for ai processing.. Understanding this is crucial for maintaining AI security and compliance.
Remova's platform is built to natively manage and optimize Embedding through our integrated governance layer, ensuring that your organization benefits from this technology while mitigating its inherent risks.
You can explore our full AI for companies glossary, which includes detailed definitions for related concepts like Vector Database and Retrieval-Augmented Generation (RAG).

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