AI Glossary
Retrieval-Augmented Generation (RAG)
What is Retrieval-Augmented Generation (RAG)?
An architecture that enhances LLM outputs by retrieving relevant documents from an external knowledge base before generating a response. RAG reduces hallucination by grounding the model's output in retrieved factual content. It is the standard approach for building AI systems that need access to proprietary or up-to-date knowledge without full model fine-tuning.
Example in practice
A consulting firm's AI assistant that automatically retrieves the three most relevant past project reports before answering a methodology question has implemented RAG — grounding each answer in real precedents.
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See Retrieval-Augmented Generation (RAG) applied in a professional context through this free course.
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