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AI Glossary

Retrieval-Augmented Generation (RAG)

A method where AI responses are informed by retrieved reference content.

TL;DR

  • —A method where AI responses are informed by retrieved reference content.
  • —Retrieval-Augmented Generation (RAG) shapes how organizations design controls, ownership, and operating discipline around AI.
  • —Use the related terms and explanation below to connect the definition to real enterprise rollout decisions.

In Depth

RAG workflows retrieve relevant documents at response time and supply that context to the model so outputs can be informed by current reference material. In enterprise settings, RAG is often used to reduce unsupported answers, improve factual alignment with internal knowledge, and keep assistants useful without retraining a model. Good RAG design still needs governance around document quality, permissioning, and how retrieved evidence is surfaced to users.

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Related Terms

Knowledge Grounding

Using approved internal context to improve response relevance in AI workflows.

AI Governance

The policies, controls, and operating practices used to manage AI usage safely at scale.

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Glossary FAQs

Retrieval-Augmented Generation (RAG) matters because it directly affects how teams operationalize AI safely, how leaders assign ownership, and how controls are applied in daily workflows. Organizations that misunderstand retrieval-augmented generation (rag) usually end up with inconsistent rollout decisions and weaker governance discipline.
Remova supports retrieval-augmented generation (rag) through policy controls, role-based access, auditability, and workflow governance features that make the concept operational rather than theoretical. The goal is to give teams a controlled way to apply the principle in production usage.
You can explore related entries in the glossary, including Knowledge Grounding and AI Governance, to see how this concept connects to broader enterprise AI governance and operating practices.

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