Grounding Language Models: How RAG Prevents Hallucination and Enables Current Knowledge

Large language models frequently hallucinate when asked about information beyond their training data—a critical limitation for enterprise applications. RAG (Retrieval-Augmented Generation) solves this by augmenting prompts with relevant context from external knowledge bases, enabling models to ground responses in accurate, up-to-date information without costly retraining. This transforms AI from an unreliable system into a practical tool for organizations that require reliability and current knowledge.