Retrieval-augmented generation (RAG) is a pattern where a system searches a knowledge base for relevant passages, then conditions the language model on those passages when answering. It aims to ground responses in your documents without permanently baking every fact into model weights.
RAG is the industry’s favorite answer to “how do we put our PDFs in the chatbot?” — search first, then generate.
Quality usually fails at retrieval: chunking, stale indexes, and permission filters matter more than swapping the base model.
When Latent covers enterprise copilots, RAG architecture is often the difference between a demo and a system legal will allow.
Regional funds are concentrating on Spanish-language enterprise tools.
It reduces some fabrications by supplying sources, but models can still misread or ignore retrieved text. Citation quality and evaluation still matter.
A document store, an embedding/search stack, a prompt that inserts retrieved chunks, and monitoring for empty or wrong retrievals.