A language model that generates text purely from its parametric memory can produce confident but incorrect statements — hallucinations. Grounding addresses this by tethering generation to external, verifiable sources: retrieved documents, structured database records, or live web content. The model is instructed to base its response on the provided sources and to cite them, allowing the user to verify the output independently.
Grounding is the goal that RAG achieves technically: the retrieval step provides the ground-truth documents, and the model generates within the constraints of those documents rather than from free imagination. Grounding quality is measured by the degree to which generated claims are supported by cited sources.
For web scraping platforms, grounding is a key value proposition: AI agents equipped with live web scraping can ground their responses in current web data — today's prices, current news, live product availability — rather than training-time snapshots. This makes AI outputs trustworthy for time-sensitive queries.