What is Grounding?
Also called AI grounding
Definition
Grounding is the process of connecting an AI response to identifiable evidence or an external reference. In search applications, it usually means basing claims on retrieved sources rather than relying only on learned model associations.
How does Grounding work?
An application can ground an answer by retrieving authoritative documents, supplying relevant passages, and instructing the model to stay within the evidence. It may also attach citations that connect statements to source pages.
Grounding is broader than citation display because a linked page might not support the associated claim. The important relationship is between what the answer asserts and what the evidence establishes.
Source selection, context construction, and generation behavior all affect that relationship.
Why does Grounding matter for B2B SaaS?
B2B SaaS decisions can depend on precise statements about data handling, feature availability, and technical requirements.
An answer that sounds confident but lacks evidence can create an inaccurate shortlist or an unsuitable implementation plan. Grounded responses make verification easier and can expose where public documentation is incomplete.
Vendors should publish direct support for consequential claims rather than assume a category association is enough.
Reliable evidence is especially important when the answer compares products with different deployment models or contractual conditions.
What is an example of Grounding?
A buyer asks whether a hypothetical SaaS platform supports customer-managed encryption keys. A grounded answer cites documentation explaining the capability, eligible deployment options, and prerequisites.
If the documentation only discusses encryption in transit, it does not support the stronger claim about customer-managed keys. A generated response that attaches that page anyway is poorly grounded for this question.
The source may be legitimate while still being insufficient for the specific assertion made in the answer.
How do you improve or measure Grounding?
Assess grounding at the claim level. Identify the factual assertions that influence the decision, locate their cited passages, and check whether those passages support the wording and qualifications.
Look for omitted conditions as well as outright contradictions. In an internal assistant, test whether the model declines unsupported questions.
In public visibility work, improve documentation that repeatedly leaves material claims ambiguous. Do not use citation counts as a substitute for this evidence-quality review.
- Match important answer claims to the exact evidence that supports them.
- Preserve plan, region, deployment, and version conditions where they matter.
- Distinguish a relevant source from a source sufficient to prove the claim.
Frequently asked questions about Grounding
Is grounding the same as RAG?
No. RAG is one way to supply external context. Grounding describes the connection between the response and evidence, which can involve other tools or structured information.
Does a citation prove an answer is grounded?
Not by itself. The cited page may be outdated, loosely related, or misinterpreted. Check that the evidence supports the particular statement being evaluated.
Can grounded answers still be wrong?
Yes. Sources can be wrong or incomplete, and the model can misuse them. Grounding improves traceability but does not remove the need to assess source quality.