O que é RAG?
Em termos técnicos, Retrieval-augmented generation is an architecture that retrieves external information at query time and supplies it as context to a generative model before or during response generation.
Por que isso é importante?
Isso é importante porque RAG grounds model output in current, private, or domain-specific knowledge without placing every fact in model weights. It can improve factuality and sourceability, but only when retrieval quality and context use are reliable.
Como funciona
O sistema funciona assim: The system transforms a query, retrieves relevant documents or records, selects and formats context, sends that context with the request to the model, and returns an answer that may include citations.
Exemplo técnico
Exemplo: A support assistant retrieves the current refund policy and the customer's plan before drafting an answer, rather than relying on the model's general memory.
Notas de implementação
Notas técnicas: Measure retrieval recall separately from answer quality, preserve permissions and provenance, control chunking and context size, mitigate prompt injection in retrieved content, and require the model to acknowledge insufficient evidence.
