¿Qué es RAG?
En términos 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 qué es importante?
Es 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.
Cómo funciona
El sistema funciona así: 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.
Ejemplo técnico
Ejemplo: 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 implementación
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.
