What is a grounded AI answer?
A grounded answer is based on relevant source information supplied to the model. In a company assistant, that often means finding permitted documents first, then answering with references the reader can check.
By Exemplary AI · Updated · Illustrated explainer
The key idea
The answer should follow the evidence, and the reader should be able to inspect that evidence.
The idea, illustrated.
An answer with a visible path to its source
Figure 07“Hotel bookings above the city allowance need prior approval.”
- 01
Find permitted information
Search within the material the person and application are allowed to use.
- 02
Give the model evidence
Supply relevant passages with the question, rather than expecting the model to recall a current company rule.
- 03
Make the answer checkable
Show the source and preserve conditions, exceptions and missing information.
Where RAG fits
Retrieval-augmented generation, usually shortened to RAG, combines finding information with generating text. The original RAG paper studied a model working with retrieved passages rather than relying only on information represented in its trained parameters.
In an enterprise system, this is a useful pattern for company information that changes. Updating a source and making the new version searchable can change the evidence available to a future request. It does not require the model to be retrained for every policy change.
Primary source: Lewis et al.: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Grounding is a chain of checks
The search must find the right material. The source must be current enough for the question. The model must read it correctly. The answer must keep important qualifications. A failure at any point can produce an answer that looks supported but is misleading.
For example, a policy may cover overnight travel but say nothing about a daily commute. Citing that policy does not justify claiming that commuting costs are covered. A useful assistant distinguishes what the source says from what it does not settle. Where documents disagree, it should surface the conflict rather than silently choose the most convenient sentence.
An everyday example
“Can I book this hotel?”
The assistant finds the current travel policy and explains the applicable rate limit and approval requirement. It links to the policy so the employee can inspect the original wording.
If the city has no published allowance, the answer identifies the gap and the next person to ask. It does not manufacture a rate from a similar city.
A common misconception
“An answer with a citation cannot be made up.”
A citation can point to a real document that does not support the claim. Check whether the source actually says what the answer says, including its exceptions.
How Exemplary helps
Company knowledge, with the source close by.
Exemplary connects agents to File store knowledge and source references. We help organise the information and configure access, so your team can ask questions and inspect the documents behind the answer.
Explore knowledge in File storeSources & further reading
Primary publications and documentation. Accessed 22 September 2026.
- 01Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis et al. · NeurIPS · 2020