Introduction to RAG

RAG (Retrieval-Augmented Generation) RAG enables AI assistants to answer questions using large document collections that exceed an LLM's context window. Instead of reading all documents at once, it retrieves only the most relevant information. How it works: 1. Retrieval: Convert documents into vector embeddings and use semantic search to find relevant content. 2. Augmentation: Add the retrieved information to the AI's prompt. 3. Generation: The AI generates an accurate response based on the retrieved context. For good performance, RAG requires proper chunking, embedding model selection, and retrieval settings.