AI & Agentic Work · Last reviewed August 6, 2026
What is Retrieval-Augmented Generation (RAG)?
Retrieval-augmented generation, or RAG, is a method that finds relevant information from selected sources and supplies it to an AI model when generating an answer. The model can then respond using current, company-specific, or domain-specific evidence that was not fully contained in its original training data.
Why does Retrieval-Augmented Generation matter?
RAG can improve relevance and traceability, but poor source quality, retrieval, permissions, or freshness can still produce weak answers.
How does Retrieval-Augmented Generation (RAG) work?
Curate authoritative sources, attach metadata and permissions, test retrieval quality, show citations, and measure whether the right evidence supports the answer.
RAG quality chain
- Source quality → permissions and metadata → retrieval relevance → answer quality → citation accuracy.
- A failure at any step can produce a fluent but weak answer.
- Test retrieval separately from generation so the team knows whether the system found the wrong evidence or misused the right evidence.
Related terms
- Marketing Brain
- Vector Database
- AI Hallucination
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Explore Duet’s Marketing BrainBy Duet Editorial Team · Last reviewed August 6, 2026