Grounding

Grounding is tying an AI model's generated output to specific external sources — retrieved, looked up, or supplied at query time — so the answer reflects, and can cite, real documents and data instead of unverified model memory. Retrieval-augmented generation (RAG) is the leading grounding method, but not the only one.

Reviewed June 2026

Why it matters

When an engine grounds an answer in your published spec table, it both quotes you and reduces its risk of stating a wrong figure under your brand. Structured, unambiguous reference data is what makes a page easy to ground against.

In practice

Grainger, a major North American industrial MRO distributor managing about 2.5 million products with roughly 400,000 updates daily, built a RAG system that grounds its product-discovery answers in its own live catalog (via Databricks Vector Search) rather than the LLM's general training data. Grounding is what lets a shopper search for a specific part — say a hydraulic gear pump at a given flow rate and pressure with an SAE flange mount — and get matched to real, in-stock SKUs with current specs instead of a plausible-sounding but nonexistent part.

Source: zenml.io

Related terms

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Grounding

Why it matters

In practice

Retrieval-augmented generation (RAG)

LLM citation

Citation engineering

15 minutes. The one constraint.