Retrieval Augmented Generation (RAG) for Factual Grounding

Retrieval Augmented Generation, in a practical sense, means providing the AI with specific, relevant documents or data for it to reference *before* it generates its answer. Instead of relying solely on its internal training data, the AI uses the provided external information to formulate its response, ensuring factual accuracy and currency. For example, when asking an AI to summarize a meeting, providing the meeting transcript directly ensures the summary is grounded in what was actually said, rather than the AI hallucinating details. Before RAG, an AI might invent facts about a meeting; after, it accurately reflects the transcript.