Grounding

What is grounding?

Sometimes a large model talks convincingly about complete nonsense — not because it's lying, but because it only predicts the next word, and something that reads well isn't necessarily true. Grounding ties a rope to its answers: before responding, the model checks real data or retrieved sources, so its conclusions rest on facts instead of improvising.

How does it relate to RAG?

People mix these two up. RAG — retrieval-augmented generation — is a specific technical recipe: retrieve relevant material, then generate. Grounding is broader: anything that constrains a model's output with real information counts, including citing sources, checking against a database, or calling tools for live data.

Common ways to ground a model

Retrieval grounding
Search out relevant documents first, then have the model answer only from those docs — and cite its sources.
Tool grounding
Let the model call a calculator, a search engine or an API and answer from what the tool returns, rather than doing mental math.
Database grounding
Confine answers to a knowledge base or structured data, and say “I don't know” when the question goes beyond it.

Why it matters more and more

In medicine, finance and law — fields with no room for error — one fabricated answer can be a disaster. Grounding turns the AI from “willing to say” into “able to back it up”, the safety rail that makes large models ready for production.

Bottom line: grounding keeps the AI's feet on the ground — check first, then speak.