RAG

RAG is a pattern where you first retrieve relevant documents (code files, docs, database records) and inject them into the AI's prompt before generating a response. Instead of relying solely on what the model learned during training, RAG grounds responses in your actual data. Cursor uses RAG when it indexes your codebase and pulls relevant files into context.

It's also the standard approach for building AI chatbots that answer questions about your product.