Learn Mistral

6

Unpacking RAG Workflows

The journey of discovery lies not in seeking new landscapes, but in having new eyes.

— Marcel Proust

Language models are typically trained on large volumes of publicly available data. While this gives them a broad understanding of the world, it also means their knowledge is frozen at the moment training ends. But what if you have your own knowledge—internal documentation, private datasets, or domain-specific facts—and you want the model to generate answers based on that? What if you need every user query to be grounded in facts, checked against your sources, and shaped by up-to-date information? This is exactly where Retrieval-Augmented Generation (RAG) comes in.

This chapter is your guide to navigating RAG as if ...