Building Agentic RAG with Llamaindex Course Overview

Certainly! Below are the minimum required prerequisites for successfully undertaking training in the "Building Agentic RAG with Llamaindex" course:

• Basic Understanding of Programming: Familiarity with programming concepts and basic coding skills in at least one programming language (preferably Python, due to its wide use in data science and AI).
• Familiarity with Data Handling: Knowledge of basic data handling techniques, as the course will involve managing and manipulating data.
• Interest in AI and Machine Learning: While advanced knowledge is not strictly necessary, an interest and basic understanding of AI principles will help in grasping the course content more effectively.
• Problem-Solving Skills: Ability to think logically and solve problems, as the course involves building frameworks that require analytical thinking.

These prerequisites are aimed to ensure that all participants have a foundational knowledge that will help them to effectively engage with the course material and maximize their learning outcomes.

Target Audience for Building Agentic RAG with Llamaindex

Learn to leverage the power of LlamaIndex in creating Agentic RAG for advanced data reasoning and tool application, enhancing query and decision-making strategies. Ideal for IT professionals focusing on data analysis and AI development.

• Data Scientists
• AI Researchers
• Machine Learning Engineers
• Software Developers interested in AI applications
• IT Analysts specializing in data-intensive technologies
• Product Managers in tech companies focusing on AI-driven features
• Academic Researchers in Computer Science
• Technical Project Managers managing AI projects
• Data Consultants advising on data structuring and intelligence systems
• Innovation Directors seeking to implement advanced data analysis tools in business solutions

Learning Objectives - What you will Learn in this Building Agentic RAG with Llamaindex?

Introduction to Course Learning Outcomes: In this course, participants will learn to utilize LlamaIndex for developing agentic RAG capabilities, focusing on tool use, reasoning, and multi-document data handling to build sophisticated research agents.

Learning Objectives and Outcomes:

• Understand the fundamentals of LlamaIndex and its application in building agentic research agents.
• Construct the basic form of an agentic RAG, specifically a router, to direct queries to appropriate query engines based on the nature of the query.
• Implement tool calling in the router to enhance its query handling capabilities by selecting and passing appropriate function arguments.
• Develop a research assistant agent capable of engaging in a multi-step reasoning process for comprehensive tool interaction and data analysis.
• Extend the capabilities of a research agent to effectively manage and analyze information across multiple documents.
• Learn to program an agent that autonomously adapts its search and analysis strategies based on initial outputs.
• Gain expertise in crafting agents that go beyond conventional RAG systems, aiming for deeper data interaction.
• Enhance your ability to design intelligent systems that improve decision-making processes and research efficiency.
• Acquire skills in integrating advanced reasoning loops within an agent to navigate through complex information landscapes.
• Prepare to build data-driven models that are highly autonomous