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Learner Reviews & Feedback for Building Agentic RAG with LlamaIndex by DeepLearning.AI

4.8
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28 ratings

About the Course

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!...

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1 - 3 of 3 Reviews for Building Agentic RAG with LlamaIndex

By Joel F

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Jan 26, 2025

The projects allow me to immerse myself on how to create one. The lab does take about an hour , but my learning was cemented when I recreated the lab in my personal laptop. When I recreated the lab at home with school and work pdfs it took me at least two days to have the results like the lab, because as a beginner I did not know the reasoning behind why we need to create a virtual environment for projects, and to keep projects usually away at the global level in our machines (laptops). I learned about GNU, Ubuntu manual, Jupyter lite and notebook, and more. I learned about the importance of reproducibility, and how you can have pip create a requirement txt and allow others to recreate the same dependencies to have the same results. The lab is short and easily digestible, and I encourage others to attempt to recreate the lab at home with their own laptop.

By Juan B S P

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Jul 30, 2024

Really cool small project, it helped me learn the main uses and tools I can use to develop RAG systems, thanks DeepLearning.AI

By ROGONDINO A

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Jan 7, 2025

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