Retrieval-Augmented Generation (RAG) improves AI accuracy

Retrieval-Augmented Generation (RAG) is a technique designed to improve the accuracy of Large Language Models (LLMs) by allowing them to consult specific documents before generating a response. Rather than relying solely on the information memorized during their initial training, RAG enables an AI to search through a provided library of text to find relevant fragments, which helps reduce the occurrence of hallucinations.

The process typically involves breaking documents into smaller chunks, indexing them through numerical representations called embeddings, and then retrieving the most relevant pieces to inform the model's output. This method allows AI to access private company policies, recent updates, or specialized knowledge that was not included in its original training data, making the generated text more specific, diverse, and factual.