Grounding (AI)

A language model trained on internet data has broad knowledge but no way to distinguish its accurate knowledge from its inaccurate knowledge. Grounding gives the model a factual anchor. Instead of generating from memory, it generates from sources you provide.

The most common grounding technique is RAG. You retrieve relevant documents and include them in the prompt, then instruct the model to answer only based on those documents. This turns the model from a knowledge source into a reasoning engine that works with your data.

But grounding goes beyond just retrieval. It also includes verification. After the model generates a response, you can check whether the claims in the response actually appear in the source documents. This is sometimes called "attribution" or "citation verification." If the model says "Our return policy allows 30-day returns," you check that this claim exists in the retrieved policy document.

Enterprise grounding often involves multiple layers. The first layer retrieves relevant context. The second layer instructs the model to cite its sources. The third layer programmatically verifies those citations. The fourth layer flags ungrounded claims for human review.

Well-grounded AI systems are more trustworthy and auditable. When every claim can be traced back to a source document, you can explain why the system said what it said. This matters for compliance, customer trust, and internal confidence in the AI system.