Why Grounding matters at work
Grounding helps teams distinguish between a plausible answer and an answer supported by their actual documents, systems, or approved instructions. It is a core control for reliable workplace AI.
A practical workplace example
Example
Before answering a policy question, an AI retrieves the current policy page and cites the relevant section rather than relying on general model knowledge.
What teams should evaluate
- 01
Track source ownership, freshness, access controls, and retention instead of treating every connected document as equally trustworthy.
- 02
Evaluate retrieval and citation quality separately from the final answer so missing context is not mistaken for a model failure.
- 03
Protect confidential and personal information throughout indexing, retrieval, logging, and deletion workflows.
Frequently asked questions
What is Grounding in simple terms?
Grounding is the practice of basing an AI response or action on specific, relevant source information.
Why does Grounding matter for teams using AI?
Grounding helps teams distinguish between a plausible answer and an answer supported by their actual documents, systems, or approved instructions. It is a core control for reliable workplace AI.
What is a practical example of Grounding?
Before answering a policy question, an AI retrieves the current policy page and cites the relevant section rather than relying on general model knowledge.
Sources and further reading
Luffy writes every definition in plain language and checks it against primary research or authoritative technical guidance. Source links open in a new tab.