RAG (Retrieval-Augmented Generation)

A technique that gives AI models access to external knowledge by retrieving relevant documents before generating responses.

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AI Confidence: 85%

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What It Means

RAG combines a language model with a search system. When you ask a question, the system first retrieves relevant documents from a knowledge base, then feeds those documents to the language model along with your question. The model generates its answer using both its training knowledge and the retrieved information.

Why It Matters

RAG solves the "stale knowledge" problem. Language models only know what was in their training data, which has a cutoff date. RAG lets them access current information without retraining. It also reduces hallucinations by grounding responses in actual documents. Most enterprise AI applications use RAG.

Sources & Further Reading

Lewis et al., "Retrieval-Augmented Generation" - https://arxiv.org/abs/2005.11401

Pinecone: "What is RAG?" - https://www.pinecone.io/learn/retrieval-augmented-generation/