Grounding
Grounding is a technique that enables every fact and intermediate conclusion in an AI workflow to be traced back and anchored to the original data source, used to reduce the risk of cascading errors in long-chain tasks.
Timeline (last 90 days)
The grounding mechanism anchors every fact in a workflow to its original source, while the memory system continuously retains enterprise background information; the combination of the two is the key support for reliable execution of long-horizon tasks.
The "citation" mechanism in LLM structured extraction requires the model, when outputting each extracted field, to provide the verbatim source snippet in the original document for that value, based on the theoretical rationale that accurately copying source text reduces hallucination risk.
The citation mechanism belongs to "grounded generation," which can reduce hallucination risk, satisfy enterprise compliance and audit requirements, and help users quickly jump to the original document.
All Facts (3)
The grounding mechanism anchors every fact in a workflow to its original source, while the memory system continuously retains enterprise background information; the combination of the two is the key support for reliable execution of long-horizon tasks.
50%UnverifiedThe "citation" mechanism in LLM structured extraction requires the model, when outputting each extracted field, to provide the verbatim source snippet in the original document for that value, based on the theoretical rationale that accurately copying source text reduces hallucination risk.
50%UnverifiedThe citation mechanism belongs to "grounded generation," which can reduce hallucination risk, satisfy enterprise compliance and audit requirements, and help users quickly jump to the original document.
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