{"id":41317,"date":"2025-10-27T16:17:49","date_gmt":"2025-10-27T15:17:49","guid":{"rendered":"https:\/\/csw.agency\/glossar\/ai-grounding\/"},"modified":"2025-10-27T16:36:00","modified_gmt":"2025-10-27T15:36:00","slug":"ai-grounding","status":"publish","type":"glossar","link":"https:\/\/csw.agency\/en\/glossar\/ai-grounding\/","title":{"rendered":"AI Grounding"},"content":{"rendered":"

AI grounding is a central concept in development and application Artificial intelligence (AI)<\/a>. It refers to the process of linking abstract symbols, linguistic representations or the output of an AI system with concrete, verifiable data from the real world or specific knowledge sources. The aim is to ensure the factual accuracy, contextual relevance and reliability of AI-generated content.<\/p>\n

AI models, especially large Language models (Large Language Models, LLMs)<\/a>, are primarily designed to generate probable word sequences based on large training databases. This can lead to them producing convincing-sounding but factually incorrect or fabricated information, so-called \u201ehallucinations\u201c. Grounding counteracts this tendency by encouraging the AI to \u201eground\u201c its answers to external, trustworthy and often up-to-date data sources.<\/p>\n

How AI Grounding works<\/h2>\n

The grounding of AI systems works via various methods that aim to build a bridge between the internal model logic and the external reality. One key method is retrieval augmented generation (RAG). Here, AI models specifically retrieve information from external knowledge databases during answer generation and integrate it. This enables the model to respond not only on the basis of its original training knowledge, but also to access current or company-specific information.<\/p>\n

Other grounding methods include:<\/p>\n