Does grounding a model in your own data guarantee correct answers?
Why D is the answer
No, it reduces the risk but cannot remove it
Microsoft says it can reduce, but not completely eliminate, the likelihood of inaccurate responses. Keep human review.
In detail
- Grounding reduces the risk of wrong answers.
- It cannot remove it, so people still review what matters.
- Show sources, so people can verify answers.
Use case
An illustrative scenario, not a real incident.
A grounded bot still misreads a policy clause. Grounding lowers the risk of wrong answers, so important answers still get checked.
See it in different ways
sequenceDiagram participant U as User participant M as Model participant K as Knowledge source U->>M: Question M->>K: Finds relevant content K-->>M: Passages M-->>U: Answer based on them U->>U: Still checks what matters
flowchart TD
Q{"Is the answer important?"}
Q -->|"Yes"| A0["A person checks it"]
Q -->|"No"| A1["Sample occasionally"]Key point: Grounding reduces risk but cannot remove it.
Why the other options are wrong
- A. No, because grounding makes answers less accurate
- Grounding improves accuracy.
- B. Yes, grounded answers cannot be wrong
- Grounded answers can still be wrong.
- C. Yes, as long as the data is in English
- Language has no bearing on this.