Grounding is the process of tying an AI response to verifiable reference information. The goal is for the output to be supported by relevant sources, such as internal documents or search results, rather than relying solely on patterns the model has learned.
How is grounding applied?
The application provides evidence and requires the response to align with it. Traceability means identifying which source supports each claim; showing links without that connection does not solve the problem.
How is it applied in marketing?
To explain a rate, an assistant uses the approved list and cites its version. If the query asks about a service that is not included, it points out the gap instead of assigning a guessed price.
| Concept | Meaning |
|---|---|
| Grounding | Connects a response to evidence. |
| RAG | Retrieves sources and incorporates them into generation. |
Frequently asked questions about grounding
Are grounding and RAG synonyms?
No. RAG is a retrieval technique; grounding describes the relationship between the output and reference information.
Does a link demonstrate grounding?
Only if it supports the corresponding claim. Check that it exists, is relevant, and has been interpreted correctly.
Do the sources have to be public?
Not necessarily. They can be internal documents, as long as their access and use are authorized.
What happens with an old source?
It can support an outdated response. Keep versions and review effective dates according to the topic.
How do you verify fidelity?
Compare claims with the original passages. Include tests in which the source does not contain the answer.
Grounding: limits you should know
A response connected to sources can still misinterpret them. Review currency, scope, integrity of the excerpt, and fidelity of the summary.
To expand the context, compare this concept with RAG, AI hallucination and Embeddings.
Reference source: docs.cloud.google.com. AMD Digital Agency Glossary.
