Agents leverage Retrieval Augmented Generation (RAG) to prevent hallucination by grounding their responses in external, factual data. The process involves a retrieval component that first queries a curated knowledge base or database to fetch relevant documents. These retrieved documents then serve as the primary context for the language model, rather than relying solely on its internal, potentially outdated or generalized training data. The agent is specifically instructed to synthesize answers strictly from the provided information, thereby limiting its ability to invent details. This approach ensures that the output is directly attributable to verifiable sources, significantly reducing the likelihood of generating inaccurate or fabricated content. Additionally, systems often include mechanisms like explicit citation generation, confidence scoring, and human-in-the-loop validation to further enhance source traceability and factual accuracy. More details: https://247tienganh.com/Home/ChangeLanguage?lang=en-US&url=https://infoguide.com.ua/