f you have asked ChatGPT a question about a specific internal company policy or a recent event and received a convincing but completely incorrect answer, you have experienced the “Hallucination Problem.”
Large Language Models (LLMs) are brilliant, but they are frozen in time. They only know what they learned during training. This is where Retrieval-Augmented Generation (RAG) changes the game.
RAG is the architecture that bridges the gap between the raw power of generative AI and the specific, factual knowledge contained in your private databases. Instead of relying solely on “memorized” data, RAG gives the AI a “cheat sheet” to read before it speaks. Let’s dive into how it works and why it is the most important AI trend of the year.