Grounding

A grounded model is not asked what is true. It is handed source material and asked to write from it: a company’s product sheets, a set of research papers, a search index, or a knowledge graph built from any of those. The output can only be as expansive as the sources allow, and when the sources do not support a claim, a well-grounded system leaves the claim out instead of filling the gap.

Grounding is a property of the output, not a particular technique. Retrieval-augmented generation is the most common way to achieve it, but a model given a fixed document to summarise is also grounded. The test is simple: can every factual sentence be matched to the passage it came from?

Fluent prose is not evidence. A general model writes what is statistically likely about a company like yours, which is how AI writers invent features, misquote prices and attribute findings that were never published. In marketing that is not a style problem. It is a claim published under your name that a customer may act on.

Grounding is also becoming what the answer engines themselves do. Google’s own developer documentation describes grounding as the way to reduce hallucinations and to show the sources behind a model’s claims, which is the same standard a reader applies to your content when they decide whether to trust it.

• Google Cloud’s documentation defines it plainly: “In generative AI, grounding is the ability to connect model output to verifiable sources of information.”Google Cloud, Grounding overview, 15 Sept 2026
• The same page lists the first benefit as: “Reduces model hallucinations, which are instances where the model generates content that isn’t factual.”Google Cloud, Grounding overview, 15 Sept 2026
• Google’s Gemini API guide lists “Provide citations: Build user trust by showing the sources for the model’s claims” among what grounding lets an application do.Google AI for Developers, Grounding with Google Search, 15 Sept 2026

1. 1

   Decide what the approved sources are before anyone writes: current product documentation, pricing, published research and customer-approved quotes. Anything not in that set is not a source.

2. 2

   Keep the sources in one place the tool can read, and remove superseded versions so an old price cannot be retrieved alongside the new one.

3. 3

   Require a citation on every factual claim in a draft, linked to the passage it came from, not only a list of documents at the end.

4. 4

   Review by opening citations, not by rereading prose. A claim without one is either cut or sent back for a source.

5. 5

   Treat an answer of “the sources do not cover this” as a correct result, and fill the gap by adding a source, not by letting the model guess.

• Pasting documents into a prompt and calling the result grounded. Without per-claim citations, you cannot tell which sentences came from the documents and which came from the model.
• Grounding in the open web when the claims are about your own product. Search results describe competitors, old versions and third-party opinions as confidently as they describe you.
• Checking that citations exist but not that they support the sentence. A citation to the right document can still sit beside a claim the document never makes.

Retrieval-augmented generation

RAG is a method: fetch relevant documents, then write from them. Grounding is the result you want from it. RAG without citations can still produce ungrounded sentences.

Fact-checking

Fact-checking happens after writing and catches what a reviewer notices. Grounding constrains the writing itself, so there is less to catch.

1. 1Google Cloud, Grounding overview read 15 Sept 2026
2. 2Google AI for Developers, Grounding with Google Search read 15 Sept 2026