Grounding in AI is a method that ties the answers of a language model to verifiable sources. Instead of answering from its training data alone, the system first retrieves relevant content, such as search results, database entries or internal documents, and bases its statements on it. Each statement can then be checked against a source, and the risk of invented information drops.<\/p>\n\n\n\n
The word grounding literally means earthing or anchoring. The idea is that an answer is anchored in verified facts. For Search engine optimization<\/a> the term has become important because AI search engines such as Google's AI Overviews back up their answers with sources in exactly this way.<\/p>\n\n\n\n A language model generates text by calculating the most likely next word from the patterns in its training data. It does not check whether a statement is true. If information is missing from the training data or out of date, the model still produces a fluent answer that may be wrong. Such answers are called hallucinations.<\/p>\n\n\n\n Then there is the knowledge cutoff. A model only knows what was in its data up to its training cutoff date. Prices, delivery times, legal positions and product versions keep changing after that, and without access to current sources the model cannot know about these changes.<\/p>\n\n\n\n Google documents the process for the Gemini API when the model uses Google Search as a source. It consists of five steps:<\/p>\n\n\n\n The attribution in the last step happens at the level of individual sentences. For each citation, the Gemini API marks which character range of the answer is based on which URL. Anthropic's Claude splits supplied documents into sentences and returns, for each statement, the exact passage it comes from. What gets cited is not a page as a whole but the individual sentence or section.<\/p>\n\n\n\n The methods differ mainly in where the sources come from:<\/p>\n\n\n\n For applications within a company, RAG<\/a> is the obvious choice because the sources are known and controllable. Web search fits where questions require current or public information.<\/p>\n\n\n\n No. Grounding describes the goal of tying an answer to sources. RAG is one technique for reaching that goal: a retrieval step fetches relevant text passages, and the model generates the answer from them. Grounding via web search or via tools works without a dedicated document collection and is still grounding.<\/p>\n\n\n\n Grounding is also often confused with fine-tuning. Fine-tuning retrains a model with additional data, and the knowledge then sits in the model weights. There is no source for the individual answer, and new knowledge requires new training. Grounding, by contrast, supplies the facts fresh with every request, together with a source.<\/p>\n\n\n\n AI search engines answer questions directly and cite their sources. In AI Overviews and AI Mode, Google shows links to the pages an answer is based on. A page that appears there as a source gets seen without having to rank first in the classic results.<\/p>\n\n\n\n According to Google, the requirements are simple: a page must be indexed and eligible to appear in Search with a snippet. There are no additional technical requirements and no special markup. Through Query Fan-Out<\/a> the system retrieves several related subquestions for one question and therefore shows more and more diverse links than a single search would.<\/p>\n\n\n\n For the Generative Engine Optimization (GEO)<\/a> this shifts the question. Instead of \u201cDoes the page rank for the keyword?\u201d it becomes \u201cDoes the page provide a verifiable statement on a subquestion?\u201d. How to build a page specifically as such a source is described in the guide on the Grounding Page<\/a>.<\/p>\n\n\n\n Because AI systems use individual sentences and sections as evidence, a few rules for structuring content follow from this:<\/p>\n\n\n\n No. Grounding lowers the risk because the answer is based on retrieved content. However, the model can misrepresent a source or choose an unsuitable one. The citations make such errors visible because every statement can be checked against its source.<\/p>\n\n\n\n In health and psychology, grounding refers to earthing and mindfulness techniques, and this meaning dominates German Google searches for the word on its own. In cognitive science, the symbol grounding problem describes how symbols get their meaning from their connection to the world (Harnad, 1990). In computer vision, visual grounding is the task of locating a described object in an image.<\/p>\n\n\n\n A grounding page is a dedicated page that describes an entity such as a company, a person or a product in a factual and structured way. It helps AI systems find a citable source during grounding. Structure and examples are covered in the guide on the Grounding Page<\/a>.<\/p>\n\n\n\n Grounding in der KI ist ein Verfahren, das die Antworten eines Sprachmodells an \u00fcberpr\u00fcfbare Quellen bindet. Statt nur aus seinem Trainingswissen zu antworten, ruft das System vorher passende Inhalte ab, etwa Suchergebnisse, Datenbankeintr\u00e4ge oder interne Dokumente, und st\u00fctzt seine Aussagen darauf. Jede Aussage l\u00e4sst sich so \u00fcber eine Quelle nachpr\u00fcfen, und das Risiko erfundener Angaben […]<\/p>\n","protected":false},"author":1,"featured_media":0,"template":"","class_list":["post-88755","glossar","type-glossar","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/webraketen.space\/en\/wp-json\/wp\/v2\/glossar\/88755","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/webraketen.space\/en\/wp-json\/wp\/v2\/glossar"}],"about":[{"href":"https:\/\/webraketen.space\/en\/wp-json\/wp\/v2\/types\/glossar"}],"author":[{"embeddable":true,"href":"https:\/\/webraketen.space\/en\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":5,"href":"https:\/\/webraketen.space\/en\/wp-json\/wp\/v2\/glossar\/88755\/revisions"}],"predecessor-version":[{"id":90759,"href":"https:\/\/webraketen.space\/en\/wp-json\/wp\/v2\/glossar\/88755\/revisions\/90759"}],"wp:attachment":[{"href":"https:\/\/webraketen.space\/en\/wp-json\/wp\/v2\/media?parent=88755"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Why language models make mistakes without grounding<\/h2>\n\n\n\n
How does grounding work?<\/h2>\n\n\n\n
\n
What types of grounding are there?<\/h2>\n\n\n\n
\n
Is grounding the same as RAG?<\/h2>\n\n\n\n
Why grounding matters for visibility in AI search<\/h2>\n\n\n\n
How content becomes a source for AI answers<\/h2>\n\n\n\n
\n
Where grounding reaches its limits<\/h2>\n\n\n\n
\n
Frequently asked questions about grounding<\/h2>\n\n\n\n
Does grounding prevent hallucinations completely?<\/h3>\n\n\n\n
What other meanings does grounding have?<\/h3>\n\n\n\n
What is a grounding page?<\/h3>\n\n\n\n
Sources<\/h2>\n\n\n\n
\n