New Learning Opportunity: Building Agentic RAG with LlamaIndex
Are you interested in learning how to build intelligent agents that can handle complex queries and analyze documents? If so, the new short course offered by DeepLearning.AI in collaboration with LlamaIndex might be perfect for you.
The course, titled “Building Agentic RAG with LlamaIndex,” is led by Jerry Liu, co-founder and CEO at LlamaIndex, and it’s designed to teach beginners how to create and utilize advanced research agents.
What is Agentic RAG?
Agentic RAG, or Retrieval-Augmented Generation, is a framework designed to enhance the capabilities of research agents. These agents are not just tools; they are built to perform tasks like reasoning, decision-making, and tool utilization to process and analyze data effectively. In simpler terms, these agents can understand and manipulate data to answer questions, summarize texts, and even conduct multi-document research.
