Grounded content is generated text held to supplied or verifiable facts, where any claim the source material doesn’t support is left out or flagged for a human to check.
| Term | Grounded Content |
|---|---|
| Category | AI, Content Generation and Experiments |
| Also known as | Grounding, Grounded Business Content |
| Where it appears | Supplemental Content prompt defaults |
What it means in RankGear
Grounded content is the behavior RankGear asks for by default in its Supplemental Content prompts. Rather than letting the model write freely from its training data, the prompt instructs it to build the output only from the context you provide plus facts it can verify. Anything outside that boundary is either omitted or explicitly marked as unverified, so a draft arrives already separated into what is supported and what still needs a human to confirm.
How to interpret it
Treat grounding as a constraint on the request, not a guarantee about the result. Before you apply or publish a grounded draft, read four things together: the context you supplied, the model you chose, the cost boundary you set, and the output the model returned. Grounding narrows how far the model is allowed to wander from your facts, but it does not verify those facts for you or catch every unsupported claim. The value is that the model is aiming to stay inside your evidence, which makes the parts that drift easier to spot.
Example
You paste a product’s real specifications and a short brief into a Supplemental Content prompt and ask for a page introduction. With grounding on, the draft describes the product using the specs you supplied and stops short of inventing a customer count or an award it was never told about. If the model does reach for a figure it can’t support, that figure comes back tagged for verification, so you can strike it or confirm it before the copy goes live.
Important considerations
- Grounding reduces invented claims; it does not eliminate them. AI output can still be incomplete or wrong, so verify factual statements, credentials, statistics, and citations yourself before publishing.
- The model can only ground against what you give it. Thin or inaccurate context produces thin or inaccurate output, however well the constraint is honored.
- For experiment-related generation, check that the isolation you claim actually holds — grounding on the prompt does not confirm your test design.
- Grounded output is a starting draft to inspect and revise, not finished copy to trust on sight.
Related terms
Part of the RankGear glossary · how RankGear measures · the 870 factors.