RAG is a technique where a model first retrieves relevant documents, then generates an answer grounded in them. It reduces hallucination and keeps answers tied to your own data.
In practice, Retrieval-Augmented Generation (RAG) shows up across many AI tools. Below are 8 tools where the idea is useful - open any to see it applied.
Google research notebook that answers using only the sources you upload
Conversational search engine that always shows its sources
AI assistant built into the Brave browser with privacy-first defaults
Visual map of related academic papers built from citation data
Search engine that answers questions from peer-reviewed research
Research assistant that extracts findings from papers into a table
Search API that finds pages by meaning for AI applications
Answer engine with citations that can turn research into slide decks