Also known as: Retrieval Augmented Generation (RAG) Β· retrieval-augmented generation (RAG) Β· RAG Β· Retrieval-Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is a technique that combines generative AI with retrieval from external data sources. The model uses retrieved documents, passages, or records as grounding context to produce responses that are more current, specific, and verifiable. RAG can reduce hallucinations, but its quality depends on retrieval accuracy, source quality, and how well the model uses the 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.