Augment your LLM Using Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) – Introduced by Facebook AI Research in 2020, is an architecture used to optimize the output of an LLM with dynamic, domain specific data without the need of retraining the model.
Retrieval Augmented Generation (RAG) – Introduced by Facebook AI Research in 2020, is an architecture used to optimize the output of an LLM with dynamic, domain specific data without the need of retraining the model. RAG is an end-to-end architecture that combines an information retrieval component with a response generator. In this introduction we provide a starting point using components we at NVIDIA have used internally. This workflow will jumpstart you on your LLM and RAG journey.
Learning Objectives
- Understand the basics of Retrieval Augmented Generation.
- Learn about the RAG retreival process
- Learn about NVIDIA AI Foundations and the components that constitue a RAG model.
