Building RAG Agents with LLMs
This self-paced course by NVIDIA's Deep Learning Institute (DLI) provides an in-depth exploration of building Retrieval-Augmented Generation (RAG) agents using large language models (LLMs). Participants will learn how to design and implement RAG agents that can retrieve relevant information from external sources to enhance the responses generated by LLMs. The course covers the integration of retrieval mechanisms with LLMs to create more accurate and context-aware AI agents.
Who Should Take This Course:
Developers, data scientists, and AI enthusiasts interested in enhancing LLM capabilities through retrieval-augmented techniques. A basic understanding of machine learning concepts is recommended.
What You Will Learn:
• Fundamentals of Retrieval-Augmented Generation (RAG)
• Techniques for integrating retrieval mechanisms with LLMs
• Designing and implementing RAG agents
• Best practices for building context-aware AI agents
Why This Course Matters:
As the demand for more intelligent and context-aware AI systems grows, understanding and implementing RAG techniques is crucial. This course equips learners with the knowledge to enhance LLMs, enabling them to deliver more accurate and relevant responses in various applications.