🔷Key Takeaways
| Course Name | Building RAG Agents with LLMs |
|---|---|
| Platform | NVIDIA Deep Learning Institute (DLI) |
| Price | Free (Limited Time) |
| Duration | 8 Hours |
| Level | Intermediate |
| Prerequisites | Deep learning basics, PyTorch familiarity, intermediate Python |
| Skills Learned | LLM orchestration, dialog management, document retrieval, embedding models, vector stores |
About the Course
Agents powered by large language models (LLMs) are transforming the way we retrieve and process information. This course teaches you how to build and deploy Retrieval-Augmented Generation (RAG) agents that can efficiently retrieve and structure information from documents while interacting with users. Whether you’re developing AI-powered assistants or scaling up LLM applications, this workshop provides hands-on training in advanced techniques like dialog management, embeddings, and vector stores.
What You’ll Learn
- LLM System Composition: Build an agent that interacts predictably using internal reasoning and external knowledge.
- Dialog & Document Management: Design a system that maintains state and structures information effectively.
- Embeddings & Guardrailing: Use embedding models for similarity queries and implementing safeguards.
- RAG Agent Development: Deploy a modular, evaluation-ready RAG system for research paper Q&A.
Who Should Take This Course?
- AI practitioners and engineers looking to build advanced LLM-powered agents.
- Developers with intermediate Python skills and experience in deep learning frameworks like PyTorch.
- Anyone interested in practical implementations of Retrieval-Augmented Generation (RAG) systems.
Topics Covered
- LLM inference interfaces and microservices
- Pipeline design with LangChain, Gradio, and LangServe
- Dialog state management and knowledge extraction
- Embedding models for semantic similarity and guardrailing
- Vector store implementation for efficient retrieval
Additional Details
- Duration: 8 hours
- Price: Free (Limited Time)
- Level: Intermediate
- Prerequisites: Deep learning basics, PyTorch familiarity, intermediate Python programming
- Platform: NVIDIA Deep Learning Institute (DLI)
