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    Glossary›RAG
    Glossary · AI

    What is Retrieval-Augmented Generation (RAG)?

    Definition

    Retrieval-Augmented Generation (RAG) is an AI technique that grounds language model outputs in retrieved source documents. Rather than relying solely on training data, RAG retrieves relevant information from a knowledge base before generating answers. This approach improves accuracy and enables AI to reference verified sources.

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    document retrievalsource groundingknowledge integrationAI accuracyinformation retrievalanswer verificationRAG
    In short

    RAG at a glance.

    Grounds AI answers in retrieved documents
    Improves accuracy and source reliability
    Enables AI to cite references
    Reduces AI hallucination and false claims

    AI Grounded in Facts

    Retrieval-Augmented Generation tackles a key limitation of pure language models: they sometimes generate plausible-sounding but false information. RAG retrieves relevant documents first, then generates answers based on those sources. This approach produces more accurate, verifiable answers. Organizations increasingly use RAG to power customer service, help desk, and training systems.

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    RAG — frequently asked

    RAG requires the system to find supporting documents before answering. If no relevant document exists, the system can report that rather than inventing an answer.

    Well-organized, clearly written sources work best. Knowledge bases, documentation, FAQs, and procedure guides are ideal RAG sources.

    RAG typically produces more accurate answers and is more reliable for factual questions. Performance depends on source quality and retrieval accuracy.

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