Retrieval-Augmented Generation (RAG) transforms traditional LLM systems by integrating external information for enhanced text generation. Agentic RAG further empowers these systems, enabling dynamic decision-making and complex reasoning. This self-paced workshop teaches you to build an agentic RAG using NVIDIA’s Nemotron model and LangGraph framework. You’ll gain insights into RAG architecture, setting up development environments, and configuring essential project secrets, like API keys for NVIDIA and LangSmith.
Included are comprehensive instructions for ingesting and processing data, utilizing vector databases for efficient retrieval, and creating a responsive agent capable of leveraging advanced LLM features. The course culminates in building a customized agent ready for deployment.
Engage in hands-on learning with modern AI tools while understanding the integration of retrieval mechanisms, tool calling, and the nuances of agentic behavior. Stay updated on developments in Agentic AI and explore more resources through NVIDIA’s community and educational platforms.
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