Understanding and Fixing AI Hallucinations with RAG

AI models sometimes generate false or nonsensical information, a phenomenon known as hallucination. This occurs because these models predict responses based on patterns in their training data rather than accessing a reliable knowledge base. To address this, developers use a technique called Retrieval-Augmented Generation (RAG). RAG combines a pre-trained language model with an external retrieval system that fetches relevant, up-to-date documents from a trusted database. By grounding the model's output in verifiable sources, RAG significantly reduces hallucinations. The process works in two steps: first, a retriever searches for pertinent information; second, the generator uses that context to produce a response. This approach not only improves accuracy but also allows the model to cite sources, making AI more reliable for tasks like question answering and report generation.