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    Grounding (in AI)

    In generative AI, grounding connects an output to verifiable information. This may include documents, search results or data from a business system. The connection improves checkability but does not guarantee a correct answer.

    Grounding (in AI) explained

    The central question is: what supports this statement? An answer about delivery timing should refer to the relevant current information. General model knowledge may be insufficient. A connected source is still only as dependable as its and correct identification.

    RAG is one possible form of grounding. A targeted query to a business system can be another. Grounding is a broader working principle, not a synonym for a particular database or protocol.

    Three checks help: is the source suitable for the question? Did the system actually receive it? Does it support the specific output? Having a data connection answers only part of this. Outdated information and access permissions also need to be considered.

    For marketing copy, product specifications, research findings and project statements need traceable support. A log may document the source used; that does not create automatic legal clearance.

    Examples

    Hypothetical application

    An assistant drafts a product description from an approved specification. It must not add an undocumented environmental certification because that seems common in the market. Review checks relevant product properties against the specification.

    Key Points

    • Data access and source faithfulness are separate checks.
    • RAG is one possible grounding approach.
    • Source quality and version remain important.

    Practical application

    Define acceptable sources for important types and how their use will be checked.

    Useful measures

    Supported statements

    Statements traceable to a suitable source that was actually used.

    Common mistakes

    • Presenting a data connection as a truth guarantee.
    • Confusing logging with legal approval.

    Sources and context

    Frequently Asked Questions about Grounding (in AI)

    No. Sources may be unsuitable or incorrectly used in the answer.

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