What is Grounding?
TL;DR
The technique of anchoring AI outputs to reliable external data sources to ensure factual accuracy.
Grounding: Definition & Explanation
Grounding is the technique and approach of anchoring AI model outputs to reliable external data sources and facts, ensuring the accuracy and trustworthiness of responses. Because LLMs generate answers based on patterns in training data, they are prone to hallucinations — fabricating information that sounds plausible but is not factually correct. Grounding addresses this by 'grounding' AI responses in verifiable facts through RAG (Retrieval-Augmented Generation), real-time web search, structured database queries, and knowledge graph utilization. Google Cloud Vertex AI's Grounding feature and Perplexity AI's source citation capability are notable implementations. For organizations deploying AI in business operations, implementing grounding technology is considered essential for ensuring response reliability. As of 2026, the importance of grounding in enterprise AI continues to grow.