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AI Glossary

Retrieval-Augmented Generation (RAG)

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

TL;DR

  • —A technique that grounds AI responses in retrieved documents to improve accuracy and reduce hallucinations.
  • —Understanding Retrieval-Augmented Generation (RAG) is critical for effective AI for companies.
  • —Remova helps companies implement this technology safely.

In Depth

RAG combines information retrieval with text generation. When a user asks a question, the system first searches a knowledge base of uploaded documents to find relevant information, then provides this context to the AI model along with the user's query. This grounds the AI's response in factual, organization-specific data rather than relying solely on the model's training data.

Related Terms

Vector Database

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

Knowledge Graph

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

Embedding

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

AI Hallucination

When an AI model generates factually incorrect information presented as truth.

Knowledge Hub

Glossary FAQs

Retrieval-Augmented Generation (RAG) is a fundamental concept in the AI for companies landscape because it directly impacts how organizations manage a technique that grounds ai responses in retrieved documents to improve accuracy and reduce hallucinations.. Understanding this is crucial for maintaining AI security and compliance.
Remova's platform is built to natively manage and optimize Retrieval-Augmented Generation (RAG) 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 Knowledge Graph.

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