Grounding (AI)

Grounding is the practice of tying an AI system's outputs to a specific, trusted source of truth — your own documents, data, and rules — so its answers reflect your business rather than generic internet knowledge. A grounded AI cites and works from your sources; an ungrounded one guesses.

Key characteristics

  • Answers are derived from your documents/data, not general training
  • Reduces hallucination — the AI works from real sources
  • Lets outputs reflect your products, policies, and procedures
  • Often paired with citations so claims are checkable
  • The difference between a plausible answer and a correct one

Example

A loan officer grounds an AI teammate in their file-requirements checklist. When a borrower's package arrives, the teammate checks it against those exact requirements — flagging the missing bank statement — instead of guessing what a generic loan needs.

How it relates to Kuvai

Grounding is the core of how a Kuvai teammate works: you point it at your documents, policies, and data, and it does its job against your knowledge rather than the open internet. That's why a Kuvai teammate's output reflects how your business actually operates — and why it can show its work. It drafts from your sources; you decide.

Related terms

Frequently asked questions

Grounding means tying the AI to your own trusted sources — documents, data, rules — so it answers from your business rather than generic knowledge. A Kuvai teammate is grounded in the files and policies you give it, which is what makes its work accurate to your terms instead of a plausible guess.

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Grounding in AI: What It Means & Why It Matters | Kuvai