What Does Grounding an LLM Mean?

This article defines grounding in Large Language Models (LLMs) as the process of linking them to reliable, up-to-date external data sources to ensure accurate and trustworthy outputs. It explains why grounding is necessary due to knowledge cutoffs and lack of access to private data. The article details key grounding techniques: Retrieval-Augmented Generation (RAG), fine-tuning, prompt engineering, and tool use (function calling), highlighting their strengths and use cases. It emphasizes that grounding transforms LLMs from probabilistic text generators into context-aware reasoning systems, making it essential for enterprise and real-time applications.