📝Overview

This NVIDIA Deep Learning Institute course provides comprehensive training on designing and deploying Retrieval-Augmented Generation (RAG) agents using Large Language Models. The course focuses on practical implementation of RAG systems that can answer questions about research papers and documents without requiring fine-tuning. Students learn to build modular, scalable RAG architectures using modern frameworks like LangChain, Gradio, and LangServe. The curriculum covers essential components including document ingestion, vector databases (FAISS), retrieval mechanisms, and LLM integration for generation tasks. Participants gain hands-on experience with production-ready deployment strategies, dialogue management techniques, and system scaling approaches to meet real-world user demands.

📚What You'll Learn

• Implement modular RAG architectures using LangChain and modern AI frameworks

• Build and optimize vector databases with FAISS for efficient document retrieval

• Deploy scalable RAG agents using Gradio and LangServe for production environments

• Evaluate and improve RAG system performance using established metrics and methodologies

• Design dialogue management systems for interactive question-answering applications

👥Best For

• AI engineers developing conversational AI systems

• Data scientists working with document processing and information retrieval

• Developers building production-ready AI agents and chatbots

• Researchers implementing RAG solutions for knowledge-intensive applications