Watch: How RAG Works

You are about to build a RAG pipeline piece by piece: chunking, embedding, a vector store, retrieval, and a grounded prompt. Before the parts, see the whole.

The problem: a language model was never trained on your private documents, so it cannot answer questions about them, and if it tries, it guesses. RAG fixes that in three steps, Retrieve, Augment, Generate. Press play and watch a question get answered from documents the model has never seen.