AI search · Advanced

Grounding

Grounding is connecting an AI model's answer to specific sources, usually retrieved from the web or a database at the moment of answering, so the answer reflects current facts and can cite where they came from.

By Thryv StudioUpdated

Category
AI search
Level
Advanced
Purpose
Current, verifiable answers
Example
Gemini grounded with Google Search results

What is grounding?

A language model on its own answers from what it learned in training, which may be out of date or incomplete. Grounding gives the model external evidence to base its answer on, typically search results or documents, and asks it to stick to that evidence. AI platforms offer it as a feature, for example Gemini's option to ground answers in Google Search.

Why it matters

Grounded answers are only as good as the sources they're grounded in. When an assistant searches before answering, the pages it finds shape what it says about your category and your brand. Being a clear, reliable source is how you influence grounded answers.

Frequently asked questions

Does grounding stop hallucinations?

It reduces them but doesn't remove them. The model can still misread or combine sources wrongly.

How is grounding different from RAG?

RAG is a common way to ground a model: retrieve documents, then generate from them. Grounding is the broader goal of tying answers to evidence.

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