An AI answer can be useful without being authoritative. It may bring information together, propose an approach or reveal a question. Whether its statements are correct, sufficiently supported and authoritative for a particular situation requires a separate assessment.
This distinction matters in knowledge management. Otherwise, confident wording can become an apparently reviewed state simply because it has been stored, shared or used repeatedly.
Four questions with different meanings
| Question | What it establishes | What it does not automatically establish |
|---|---|---|
| Where did the statement come from? | Origin and available context | Accuracy |
| What supports it? | Sources or subject-matter foundations | Appropriate use of those foundations |
| What was reviewed? | Scope and outcome of a particular review | Applicability beyond that scope |
| Who decided what? | An accountable decision and its scope | A universally valid truth |
A statement can have a traceable origin and still be wrong. A plausible observation can be useful without being an agreed rule. Traceability enables assessment; it does not perform that assessment itself.
Why a convincing answer is insufficient
Generative AI can present incorrect or invented information convincingly. NIST describes this risk as “Confabulation” in its Generative Artificial Intelligence Profile. Confident language is therefore not a substitute for reviewing the substance.
A citation alone is insufficient too. The cited source must exist, be accessible to the reader and support the particular claim. An answer can refer to a real document while overstating or misrepresenting its content.
A second, similar answer does not necessarily provide independent confirmation. A traceable basis matters more than the number of agreeing formulations.
A fictional example: a trial becomes an apparent rule
The notes say: “Two people tried the new document structure. One question remained open.” An AI turns this into: “The team successfully introduced the new structure.”
The second sentence reads smoothly but changes the meaning:
- A limited trial becomes an introduction.
- Two participants become the entire team.
- An unresolved question disappears.
- It claims an outcome that the notes do not establish.
A useful summary would preserve the limited scope and open question. Even then, it would initially be a draft. Subject-matter approval must not be invented.
How an insight is deliberately adopted
One possible approach separates five steps:
- Record a contribution: Keep AI output or an observation identifiable as a draft.
- Identify the basis: Record relevant material, version and limitations.
- Review statements: Assess source support, completeness and applicability to the particular situation.
- Decide on adoption: Establish responsibly what should apply and within which scope.
- Maintain the authoritative source: Adopt the accepted content deliberately, keeping unresolved points distinct.
Not every question requires an extensive approval process. Appropriate review depends on significance and consequences. A technical validation or successful build does not answer the subject-matter question.
Describe Grounding precisely
“Grounding” is used in different contexts. In AI, it can mean aligning an answer with supplied information. In a knowledge collection, it can describe an explicitly maintained relationship to subject-matter foundations.
These meanings should not quietly merge. A visible foundation relationship tells readers which content a contribution builds on. It does not automatically prove that the foundation is correct or that the contribution uses it appropriately. Indirect relationships should not be presented as additional direct foundations without review.
A useful role for AI
AI can help expose origins, compare statements with supplied material or identify unresolved questions. Its instructions should allow uncertainty rather than demand a smooth answer.
For collaborative knowledge management, the decisive issue is therefore not whether AI participates. It is whether contributions, review, decisions and authoritative knowledge remain identifiable.
How this relates to OrivelaIQ
Use knowledge in context.
OrivelaIQ keeps knowledge from your own sources, contributions and shared work context distinct. Direct Grounding relationships show subject-matter foundations within the same source. They do not replace review or authority, and are not an automatic proof of truth.
The product in detailSources and perspective
An editorial perspective by OrivelaIQ. These concepts and approaches are working models, not a universal standard.