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Jerry Liu

Join our new short course and learn from Jerry Liu, co-founder and CEO at LlamaIndex to start using agentic RAG, a framework designed to build research agents skilled in tool use, reasoning, and decision-making with your data.

In this course:

1. Build the simplest form of agentic RAG – a router. Given a query, the router will pick one of two query engines, Q&A or summarization, to execute a query over a single document.

2. Add tool calling to your router agent where you will use an LLM to not only pick a function to execute but also infer an argument to pass to the function.

Read more

Join our new short course and learn from Jerry Liu, co-founder and CEO at LlamaIndex to start using agentic RAG, a framework designed to build research agents skilled in tool use, reasoning, and decision-making with your data.

In this course:

1. Build the simplest form of agentic RAG – a router. Given a query, the router will pick one of two query engines, Q&A or summarization, to execute a query over a single document.

2. Add tool calling to your router agent where you will use an LLM to not only pick a function to execute but also infer an argument to pass to the function.

3. Build a research assistant agent. Instead of tool calling in a single-shot setting, an agent is able to reason over tools in multiple steps.

4. Build a multi-document agent where you will learn how to extend the research agent to handle multiple documents.

Unlike the standard RAG pipeline—suitable for simple queries across a few documents—this intelligent approach adapts based on initial findings to enhance further data retrieval. You’ll learn to develop an autonomous research agent, enhancing your ability to engage with and analyze your data comprehensively.

You’ll practice building agents capable of intelligently navigating, summarizing, and comparing information across multiple research papers from arXiv. Additionally, you’ll learn how to debug these agents, ensuring you can guide their actions effectively.

Explore one of the most rapidly advancing applications of agentic AI!

Enroll now

What's inside

Syllabus

Building Agentic RAG with LlamaIndex
Join our new short course and learn from Jerry Liu, co-founder and CEO at LlamaIndex to start using agentic RAG, a framework designed to build research agents skilled in tool use, reasoning, and decision-making with your data.In this course: 1. Build the simplest form of agentic RAG – a router. Given a query, the router will pick one of two query engines, Q&A or summarization, to execute a query over a single document. 2. Add tool calling to your router agent where you will use an LLM to not only pick a function to execute but also infer an argument to pass to the function. 3. Build a research assistant agent. Instead of tool calling in a single-shot setting, an agent is able to reason over tools in multiple steps. 4. Build a multi-document agent where you will learn how to extend the research agent to handle multiple documents. Unlike the standard RAG pipeline—suitable for simple queries across a few documents—this intelligent approach adapts based on initial findings to enhance further data retrieval. You’ll learn to develop an autonomous research agent, enhancing your ability to engage with and analyze your data comprehensively.You’ll practice building agents capable of intelligently navigating, summarizing, and comparing information across multiple research papers from arXiv. Additionally, you’ll learn how to debug these agents, ensuring you can guide their actions effectively. Explore one of the most rapidly advancing applications of agentic AI!

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Enhances ability to comprehensively analyze and engage with data by using the agentic Research Assistant Agent
Course focuses on using and examining an intelligent approach that adapts to enhance data retrieval, which is a rapidly advancing application of agentic AI
Teaches how to build research agents that are capable of intelligently navigating, summarizing, and comparing information across research papers
Provides practice in debugging agents to guide their actions effectively
Learners will learn to develop an autonomous research agent, which may be beneficial for their ability to analyze and engage with data
Assumes familiarity in tool calling, reasoning over tools in multiple steps, and handling multiple documents

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Agentic RAG with LlamaIndex with these activities:
Review basic programming concepts
Review the basics that you'll need to know to succeed in this course.
Browse courses on Programming Concepts
Show steps
  • Re-read your notes from a previous programming course.
  • Do practice problems on a coding website, such as LeetCode or HackerRank.
Practice building agentic RAGs
The more you practice, the better you will become at building agentic RAGs.
Browse courses on Reasoning
Show steps
  • Build an agentic RAG to answer a simple question.
  • Build an agentic RAG to solve a more complex problem.
  • Build an agentic RAG that can reason over multiple documents.
Write a blog post about your experience building an agentic RAG
This will help you to solidify your understanding of agentic RAGs and to share your knowledge with others.
Browse courses on Reasoning
Show steps
  • Choose a topic for your blog post.
  • Write a draft of your blog post.
  • Edit and publish your blog post.
One other activity
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Show all four activities
Build an agentic RAG that can be used to solve a real-world problem
This will give you a chance to apply your skills to a real-world problem and see how agentic RAGs can be used to make a difference.
Browse courses on Reasoning
Show steps
  • Identify a real-world problem that can be solved using an agentic RAG.
  • Design and build an agentic RAG to solve the problem.
  • Test and evaluate your agentic RAG.

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Learners who complete Building Agentic RAG with LlamaIndex will develop knowledge and skills that may be useful to these careers:

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