Also known as: Retrieval Augmented Generation (RAG) · retrieval-augmented generation (RAG) · RAG · Retrieval-Augmented Generation (RAG)
RAG, o generación aumentada por recuperación, es una técnica que combina IA generativa con recuperación desde fuentes de datos externas. El modelo usa documentos, pasajes o registros recuperados como contexto de grounding para producir respuestas más actuales, específicas y verificables. RAG puede reducir alucinaciones, pero su calidad depende de la precisión de la recuperación, la calidad de las fuentes y qué tan bien el modelo usa el contexto recuperado.
A technique that enables large language models (LLMs) to retrieve and incorporate new information.
A technique to improve the quality and accuracy of large language model (LLM) output by grounding it with sources of knowledge that are retrieved after the model was trained. RAG addresses LLM limitations such as factual inaccuracies, lack of access to current or specialized information, and inability to cite sources.
A side-by-side comparison of Retrieval-Augmented Generation and Fine-Tuning. Understand when to retrieve external context at runtime and when to adapt a pretrained model through additional training.
A side-by-side comparison of Information Retrieval and Retrieval Augmented Generation. It explains how finding relevant information differs from using retrieved information to ground generative AI responses.