Also known as: Retrieval Augmented Generation (RAG) · retrieval-augmented generation (RAG) · RAG · Retrieval-Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) ist eine Technik, die generative KI mit Retrieval aus externen Datenquellen kombiniert. Das Modell nutzt abgerufene Dokumente, Passagen oder Datensätze als Grounding-Kontext, um aktuellere, spezifischere und überprüfbarere Antworten zu erzeugen. RAG kann Halluzinationen reduzieren, seine Qualität hängt jedoch von der Retrieval-Genauigkeit, der Quellenqualität und davon ab, wie gut das Modell den abgerufenen Kontext nutzt.
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.