RAG

RAG (Retrieval-Augmented Generation) is a technique used to improve the accuracy, reliability, and relevance of LLMs by retrieving specific data from an external knowledge base before generating a response.

Instead of relying solely on pre-training data (which can be outdated or generic), RAG allows the model to "look up" facts in real-time from your documents, databases, or wikis. This effectively combines the reasoning power of LLMs with specific, proprietary data, significantly reducing hallucinations for business applications.