Dive into retrieval-augmented generation (RAG) to enhance large language models with dynamic, domain-specific data without needing full retraining. This advanced, self-paced course covers how to build RAG systems using NVIDIA’s tools: from indexing your documents, embedding data, performing efficient similarity retrieval via vector databases, to combining retrieved content with generative models for more accurate, context-aware outputs. Perfect for developers and AI practitioners looking to improve responses, reduce hallucinations, and integrate external knowledge sources into their LLM pipelines.
