---
title: "Grounding (AI): definition and example – Carl-Uwe Würfel"
description: "Grounding in AI explained: how language models tie answers to sources through RAG, web search or citations, how it differs from RAG and where its limits are."
url: https://carl-wuerfel.de/en/glossary/grounding
author: Carl-Uwe Würfel
---

1. [Home](https://carl-wuerfel.de/en)
  2. [Knowledge](https://carl-wuerfel.de/en/knowledge)
  3. [Glossary](https://carl-wuerfel.de/en/glossary)
  4. Grounding (AI)

# Grounding (AI)

Grounding ties a language model’s answer to verifiable sources available at the time of the request, such as documents, search results or databases, instead of training data alone.

## On this page

  1. How grounding works
  2. Grounding and RAG are not the same thing
  3. A citation shows origin, not accuracy
  4. Web search as a source
  5. What a good implementation looks like
  6. What grounding costs and what drives it
  7. Where the term stops working
  8. Frequently asked questions
  9. Sources

Grounding makes an answer checkable, not automatically correct.

_Figure: A check sequence using the example of a purchasing team that asks an internal assistant about the payment terms in a framework agreement. The system searches the contract archive, hands the model the two relevant clauses and gets back an answer in which each statement points to its clause. Two checks remain that no system does for you: whether the source is current, valid and relevant to the case, and whether the passage really says what the answer claims. The buyer opens the reference, sees the clause comes from an older contract version and has the current one added to the archive. A reference that opens with one click speeds up both checks but cannot replace them._

**Example**

A purchasing team asks an internal assistant about the payment terms in a framework agreement. The system searches the contract archive, hands the model the two relevant clauses and gets back an answer in which each statement points to its clause. The buyer opens the reference, sees the clause comes from an older contract version and has the current one added to the archive.

A language model answers from what it learned in training. That knowledge stops at a cutoff date and excludes a company’s internal information. Lacking the knowledge, the model still writes a fluent answer that can be wrong. Grounding addresses this: with every request, a system hands the model relevant sources, the model bases its answer on them and points to where each statement comes from. Google Cloud defines grounding as the ability to connect model output to verifiable sources.

Grounding is a property of the answer, not a single method. The best-known route is [RAG](https://carl-wuerfel.de/en/glossary/rag) (retrieval-augmented generation): a search of your own documents precedes the answer. Other routes are web search, as in Grounding with Google Search for Gemini or OpenAI’s web search tool, passing documents directly with a citation feature, as with Anthropic’s Citations, and queries against databases or business systems. Google’s [AI Overviews](https://carl-wuerfel.de/en/glossary/ai-overviews) follow the same idea and show supporting web pages with the answer.

For executives, grounding decides whether an AI answer can be checked. An assistant for contracts, products or policies is only as reliable as its sources. Grounding shifts the work from the model to the sources: Which are valid, who maintains them, who may see what? Hallucinations become rarer, not impossible.

## How grounding works.

A grounded answer comes together in four steps. The system receives the request and retrieves relevant sources: passages from your own documents, web search results or records from a business system. These passages go into the model’s input along with the question. Finally, the model writes the answer based on the passages and links each statement to its origin.

With web search, the model can decide whether and what to search. Google describes the flow for Grounding with Google Search like this: the model checks whether a search would improve the answer, writes its own search queries if needed, processes the results and returns an answer with inline citations.

What matters is a reference for each statement. A list of links under the answer only shows what was read. A good reference shows which sentence rests on which passage, so a person can check exactly that passage.

The no-match case matters just as much. A well-built system says plainly that no suitable source exists instead of filling the gap from training data.

## Grounding and RAG are not the same thing.

RAG is a method; grounding is the goal it serves. The term comes from a 2020 paper by Patrick Lewis and colleagues that trained a retrieval module and a language model together. Today RAG means any combination of search and a language model, usually over your own documents.

Grounding covers more. The answer can rest on a live web search, an inventory system query, a single contract placed in the context in full, or a map source. For its models, Google Cloud lists Google Search, Google Maps, your own data, Elasticsearch and custom search APIs, among others, as grounding sources. The logic is the same in every case: source first, then answer.

Many vendors simply call their RAG feature grounding, so it pays to ask which sources are connected and how the search works.

[Fine-tuning](https://carl-wuerfel.de/en/glossary/fine-tuning) is a different thing altogether. It trains the model further on additional examples and changes its style, format and vocabulary. It provides no source for an individual statement, and facts change faster than training runs. Grounding fits knowledge that changes; fine-tuning fits tone and form. The two can be combined.

## A citation shows origin, not accuracy.

A source reference looks like proof, but it does not show whether the cited passage supports the statement. For its Citations feature, Anthropic states that the API guarantees valid pointers to the documents provided. That means the cited passage exists. Whether the model read it correctly and whether the document is current are separate checks.

A 2024 Stanford study shows how wide the gap can be. Varun Magesh and colleagues tested AI legal research tools from major legal publishers that are built on RAG and marketed as avoiding [hallucinations](https://carl-wuerfel.de/en/glossary/hallucination). Depending on the tool, 17 to 33 percent of answers were hallucinations, counting answers whose source was misread or did not apply to the case. The authors stress that such an answer can still be grounded in the technical sense, because it sticks closely to the documents provided.

That leaves two checks no system does for you. The first concerns the source: Is it current, valid and relevant to the case? The second concerns the rendering: Does the passage really say what the answer claims? A reference that opens with one click speeds up both checks but cannot replace them.

## Web search as a source.

Web search brings current information into the answer that no training data contains: prices, news, new versions of laws, product data. Google offers this for Gemini as Grounding with Google Search, OpenAI as a web search tool, and Anthropic offers web search as well. The answers include citations that point to the pages found.

Vendors attach obligations (as of November 2026). OpenAI requires applications to show inline citations as prominent, clickable links. Google’s terms require displaying the search suggestions that come with the response.

The open web is a mixed source. Pages can be outdated, wrong or manipulated, even carrying hidden instructions for the model ([prompt injection](https://carl-wuerfel.de/en/glossary/prompt-injection)). For internal questions, your own maintained documents are the right source, not the web.

The same logic drives AI search. Google writes that for AI Overviews and AI Mode, its models identify supporting web pages while generating a response and display them as links. To be eligible, a page has to be indexed and allowed to appear in Search with a snippet. For businesses, this means your own website becomes a grounding source for other companies’ systems.

## What a good implementation looks like.

A good implementation starts with the sources. It is clear which documents or systems are authoritative, and each source has a visible date and an owner. Access rights apply at search time: an assistant only finds what the person asking is allowed to see.

Every answer cites sources per statement, and every source opens. If no suitable source exists, the system says so.

Faithfulness to the source gets measured. Google DeepMind and Google Research introduced the FACTS Grounding benchmark for this in December 2024: 1,719 tasks with documents of up to 32,000 [tokens](https://carl-wuerfel.de/en/glossary/token), where an answer counts as accurate only if it is fully supported by the document provided. Three language models from different vendors grade the answers. A benchmark like this does not replace your own set of real questions with known answers and sources.

Good systems score search and answer separately: right passage found, and rendered correctly?

## What grounding costs and what drives it.

Grounding is an ongoing operation, not a one-time purchase. Costs fall into four buckets.

The first is model calls. Every passage handed to the model lengthens the input and costs tokens. More context is not automatically better; a few relevant passages usually beat many half-relevant ones.

The second is search. Vendors generally charge for web search queries on top of tokens; prices are on each vendor’s pricing page. For your own documents, you pay to build and update the search index, and for vector search also to generate and store the [embeddings](https://carl-wuerfel.de/en/glossary/embeddings).

The third is source upkeep: reviewing documents, removing old versions, filling gaps, keeping permissions current. This bucket is easy to underestimate, because it never shows up on an invoice.

The fourth is human review. A good citation makes review faster; it does not make it unnecessary. Which bucket dominates depends on the volume and state of the sources, the number of requests and the requirements for permissions and traceability. It makes sense to log every call with its cost.

## Where the term stops working

Grounding makes an answer checkable, not automatically correct: if the source is outdated or wrong, the model passes the error on with a citation, and a citation only proves where something came from. Grounding differs from RAG, which is one method of achieving it, from fine-tuning, which changes the model itself, and from GUI grounding in [AI agents](https://carl-wuerfel.de/en/glossary/ki-agenten), which maps instructions to elements on a screen.

## Frequently asked questions

What does grounding mean in AI?

Grounding means that a language model ties its answer to sources provided with the request and points to those sources. The sources can be your own documents, web search results or data from a business system. The goal is an answer that can be checked.

What is the difference between grounding and RAG?

Grounding is the goal; RAG is one method of getting there. RAG combines a search, usually over your own documents, with a language model. Grounding can also come from web search, database queries or documents passed to the model in full.

Does grounding prevent hallucinations?

No, grounding makes hallucinations less frequent but does not prevent them. The model passes outdated or wrong sources on with a citation, and even a correct source can be misrendered. A 2024 Stanford study found hallucination rates of 17 to 33 percent, depending on the tool, in RAG-based legal research products.

What is Grounding with Google Search?

Grounding with Google Search connects Gemini answers to current Google Search results. The model decides whether a search helps, writes its own queries and returns an answer with citations. The terms require apps to display the accompanying search suggestions.

Grounding or fine-tuning: which fits company knowledge?

For company knowledge that changes, grounding is usually the better fit. It retrieves the current version with every request and can cite the source. Fine-tuning changes the model itself and is better suited to style, format and vocabulary.

What does grounding have to do with AI Overviews?

Google’s AI Overviews and AI Mode work on the same idea: the models identify supporting web pages while generating a response and show them as links. Only indexed pages allowed to appear with a snippet are eligible, which makes your website a potential source for AI answers.

What is GUI grounding?

In AI agents, GUI grounding means mapping an instruction to an element on the screen, such as a button or an input field. It has nothing to do with sources. In this glossary, grounding means tying answers to sources.

## Further reading

  * [**AI glossary**](https://carl-wuerfel.de/en/glossary)
  * [**AI knowledge management**](https://carl-wuerfel.de/en/ai-knowledge-management)
  * [**GEO: measuring AI visibility**](https://carl-wuerfel.de/en/geo)
  * [**RAG: Retrieval-Augmented Generation**](https://carl-wuerfel.de/en/glossary/rag)
  * [**Hallucination**](https://carl-wuerfel.de/en/glossary/hallucination)
  * [**Fine-Tuning** Term](https://carl-wuerfel.de/en/glossary/fine-tuning)
  * [**Context engineering** Term](https://carl-wuerfel.de/en/glossary/context-engineering)
  * [**AI Overviews** Term](https://carl-wuerfel.de/en/glossary/ai-overviews)

## Sources

  1. [Google Cloud: Grounding overview, Vertex AI documentation (as of November 2026)](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/grounding/overview)
  2. [Google: Grounding with Google Search, Gemini API documentation (as of November 2026)](https://ai.google.dev/gemini-api/docs/google-search)
  3. [Anthropic: Citations, Claude Platform documentation (as of November 2026)](https://platform.claude.com/docs/en/build-with-claude/citations)
  4. [OpenAI: Web search, developer documentation (as of November 2026)](https://developers.openai.com/api/docs/guides/tools-web-search)
  5. [Patrick Lewis et al.: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020](https://arxiv.org/abs/2005.11401)
  6. [Varun Magesh et al. (Stanford University): Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools, 2024](https://arxiv.org/abs/2405.20362)
  7. [Google DeepMind: FACTS Grounding: A new benchmark for evaluating the factuality of large language models, 2024](https://deepmind.google/blog/facts-grounding-a-new-benchmark-for-evaluating-the-factuality-of-large-language-models/)
  8. [Google Search Central: AI features and your website (AI Overviews and AI Mode), documentation](https://developers.google.com/search/docs/appearance/ai-features)