What is Grounding?
Grounding refers to the practice of connecting AI model outputs to verified sources, factual information, or retrieved documents, ensuring that generated content is based on concrete evidence rather than purely the model's parametric knowledge. A well-grounded response cites specific sources, references retrieved documents, or anchors claims to verifiable facts, making it possible to trace the origin of information and assess its reliability.
The concept addresses one of the key challenges with large language models: their tendency to generate plausible-sounding but potentially incorrect information (hallucinations). By grounding responses in retrieved documents or external knowledge sources, systems can significantly improve factual accuracy and provide transparency about information sources. Grounding is a core principle in retrieval-augmented generation, where retrieved documents serve as the factual foundation for generated responses.
Effective grounding involves several practices: retrieving relevant source documents before generation, instructing the model to base its responses on provided sources, including citations or references to specific sources, and implementing verification mechanisms to ensure the generated content aligns with the source material. Many production AI systems implement grounding through RAG architectures, explicit citation requirements in prompts, and post-generation verification steps that check consistency between generated claims and source documents.