Also known as: Retrieval Augmented Generation (RAG) · retrieval-augmented generation (RAG) · RAG · Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) — техника, объединяющая generative AI с retrieval из external data sources. Модель использует retrieved documents, passages или records как grounding context, чтобы формировать более current, specific и verifiable responses. RAG может снижать hallucinations, но его качество зависит от retrieval accuracy, source quality и того, насколько хорошо модель использует retrieved context.
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 technique allowing LLMs to access and reference knowledge sources to inform responses as they are being generated, enabling more up-to-date outputs.
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.