Retrieval-Augmented Generation (RAG) is a way to make AI text generation more accurate by combining a retrieval system that looks up information from external sources with a language model that generates text responses.

What makes RAG different is that traditional language models (like GPT-4) only know what they learned during training and can't access new information. RAG solves this problem by actively searching for relevant information before generating a response.

When you ask a RAG system a question, it searches through databases, documents, or other knowledge sources to find the most relevant information related to your question. It then combines this retrieved information with your original question, and the language model creates a response using both its built-in knowledge and the newly retrieved information.

RAG improves AI systems in several important ways. It provides access to up-to-date information beyond the AI's training data and reduces "hallucinations" (made-up information) by grounding responses in factual sources. It also makes AI more efficient by retrieving only the information needed for each specific question and builds trust by allowing responses to be traced back to their sources.

In the real world, RAG is valuable for customer support chatbots, medical and legal assistants, document summarization, and personalized recommendation systems. While RAG does face challenges with search quality and slight processing delays, it represents a significant advancement in making AI systems more useful, accurate, and trustworthy.