Grounding is the process of anchoring a language model's responses to real, external, and verifiable information, instead of relying solely on the knowledge learned during training. In practice, it consists of providing the model with specific sources—documents, databases, search results—on which it must base what it generates.
Its importance lies in the fact that it reduces hallucinations, that is, the plausible but false statements that models produce when they lack reliable data. By forcing the AI to cite or rely on verifiable sources, responses gain accuracy and traceability. This is especially critical in fields such as healthcare, law, or finance, where an error can have serious consequences.
A common technique to achieve this is RAG (Retrieval-Augmented Generation), which retrieves relevant fragments from a document base before generating the response. A well-grounded system usually:
- Indicate the sources consulted.
- Limit itself to what these support.
- Recognize when it does not have sufficient information.