Large Language Models (LLMs) are highly capable, but they lack access to your company's private data and are prone to making up facts ("hallucinating"). Retrieval-Augmented Generation (RAG) solves both problems. It provides a semantic lookup layer before model execution.
How RAG Works
When a user asks a question, the RAG system first searches your private documents (stored in a vector database) to retrieve the most relevant paragraphs. It then inputs those paragraphs along with the user's question into the LLM. The LLM synthesizes the retrieved text to write a highly accurate answer, quoting its sources. This grounds the AI in actual company facts.
Applications for Teams
RAG is perfect for building internal knowledge bases. Your client service reps can query years of product guides, contract terms, or past email logs to answer customer inquiries in seconds, ensuring complete policy alignment. It reduces staff search times and keeps statements accurate.
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