Grounding means connecting a model's output to verifiable sources of truth, your documents, databases, APIs, or live search results, so its answers rest on evidence rather than on whatever its training data happened to contain. An ungrounded model answers from a compressed memory of the internet as it existed at training time; a grounded system first fetches the relevant facts, instructs the model to answer only from them, and ideally cites which source supports each claim.
Grounding is the primary defense against hallucination and staleness, the two failure modes that most often sink enterprise AI. The main techniques are retrieval-augmented generation, which pulls relevant passages from a document store into the prompt; tool use, which lets the model query databases and APIs for live values instead of guessing them; and search grounding against the web for current events. Equally important is the verification half: citation requirements so users can check the source, instructions to say "I don't know" when the retrieved context does not contain the answer, and automated checks that flag responses unsupported by their sources. Grounding shifts the question from "do we trust the model?" to "do we trust our sources?", which is a question businesses actually know how to answer.
At arosplatforms grounding is a default requirement, not an option, for any client system that states facts. We build retrieval and tool access appropriate to the use case, enforce citations in customer-facing answers, and measure groundedness explicitly in evaluation, scoring whether each claim in a response is actually supported by the retrieved evidence.