flâneur

Course Detail | NVIDIA

learn.nvidia.com · 196 words · saved by 1 readers

In this introductory course, we will provide a high-level overview of Retrieval Augmented Generation and how it improves Generative AI (GenAI). Online purchases of self-paced courses is supported in select locations worldwide. Learn more here. Free courses are available for enrollment worldwide. Our apologies for the inconvenience. A self-paced course deep dive into how you can deploy an agent system in practice and scale up your LLM to meet the demands of your customers. A self-paced course deep dive into effictive prompt engineering with large language models. A self-paced course about transformer-based LLMs. A day-long workshop course on large language model (LLM) engineering. A technical blog explaining RAFT A technical blog explaining vector search and RAFT A technical blog post explaining Retrieval Augmented Generation A workflow for AI Chatbot With Retrieval-Augmented Generation A technical blog post to demystify RAG pipelines A technical blog post about deploying RAG at scale G

This course will soon be retired. Last day to enroll: July 7. Access ends December 31. About this Course 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.…

saved by

related reading