Building Agentic RAG with LlamaIndex

Course Description

This course, led by Jerry Liu, CEO of LlamaIndex, explores the creation of intelligent agents through the computational framework known as agentic RAG. Participants will discover how to build agents capable of reasoning, decision-making, and conducting Q&A on multiple documents. Starting from basic agents, the curriculum advances to more complex applications, allowing agents to handle multi-step reasoning and multi-document analysis.

What students will learn from the course:

  • Foundations of building a router agent capable of picking appropriate query engines.
  • Enhancing agents with tool calling abilities for dynamic function execution.
  • Construction of a research assistant agent that operates over multiple steps and documents.
  • Techniques for debugging and controlling the behavior of these complex agents.
  • Application of these skills in navigating, summarizing, and analyzing academic or research-oriented information.

Prerequisites or skills necessary to complete the course:

Basic knowledge of Python programming is required. Familiarity with basic artificial intelligence concepts and data handling would be beneficial but not essential.

Course Coverage:

  • Introduction to agentic RAG and its importance in building reasoning agents.
  • Developing a basic routing agent for selecting query engines based on the input query.
  • Incorporating tool calling features to dynamically choose and apply document processing functions.
  • Building a multi-step, multi-document research assistant capable of in-depth data analysis and reasoning.
  • Advanced techniques for debugging and enhancing the performance of AI agents.

Who this course is for:

This course is ideal for individuals with a foundational understanding of Python who are eager to explore advanced applications of AI in document analysis and data processing. It is particularly well-suited for students, data scientists, and researchers interested in developing autonomous agents for academic or professional use.

Real-world applications:

The skills taught in this course can be applied in various domains such as academic research, data analysis, content management, and more. Graduates will be equipped to build AI systems that can autonomously analyze large volumes of text, extract meaningful insights, and support decision-making processes in business or research environments.

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