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Mastering LlamaIndex: From Fundamentals to Building AI Apps

This course teaches how to use LlamaIndex to connect with large language models, build RAG systems, extract data, and create agentic and AI applications.

4.6

15 Lessons

Updated 1 week ago

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LEARNING OBJECTIVES

An understanding of the core architecture and use cases of LlamaIndex
Working knowledge of how to integrate any LLM with LlamaIndex for enhanced query handling
The ability to design and implement a RAG pipeline for efficient information retrieval
Proficiency in extracting structured data from unstructured text using schema-based techniques
Hands-on experience building single-agent and multi-agent systems with memory and workflow coordination
Working knowledge of monitoring, tracing, and evaluating LLM-driven applications for performance and reliability
The ability to assemble end-to-end AI solutions (e.g., Q&A systems, job application optimizers, lesson-plan generators)

Learning Roadmap

15 Lessons

Getting Started

Get an overview of the LlamaIndex course, including key topics, essential tools, and the foundational knowledge required to get started.

Introduction to the Course

Core Concepts and Using LLMs

Understand the fundamentals of LlamaIndex, how it connects with LLMs, and how to use it for AI-powered applications.

Introduction to LlamaIndex

Using LLMs with LlamaIndex

Building a RAG Pipeline

Build a retrieval-augmented generation (RAG) pipeline to enhance your AI’s ability to efficiently retrieve relevant information.

Implementing RAG with LlamaIndex

Build an LLM-powered Chatbot with RAG using LlamaIndex

Project

Premium

Extracting Structured Outputs from LLMs

Extract structured data from unstructured text to power AI-driven applications like document automation and knowledge retrieval.

Structured Data Extraction in LlamaIndex

Agents and Workflows

4 Lessons

Leverage LlamaIndex to build AI agents and workflows that automate multi-step reasoning and decision-making.

Monitoring and Evaluating LLM Applications

2 Lessons

Learn to trace, evaluate, and improve LLM applications for greater reliability and performance.

Building Real-World Applications with LlamaIndex

3 Lessons

Build complete, real-world AI applications by combining multiple LlamaIndex concepts into cohesive, interactive systems.

Wrap Up

Conclude what you’ve learned to start building smarter AI applications with LlamaIndex.

Final Thoughts

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

ABOUT THIS COURSE

This course explores LlamaIndex’s core architecture and how it connects unstructured data to language models. You’ll learn how to connect LLMs to content sources like documents, APIs, and knowledge bases. You’ll build and refine RAG systems, extract structured data, and feed results into downstream analytics or automation workflows. Finally, you’ll orchestrate AI agents and workflows, from single-agent assistants to complex multi-agent architectures with shared memory and state. You’ll add logging, tracing, and dashboards to your pipelines, then apply everything in hands-on projects like building a document Q&A system and lesson-plan generator. After completing this course, you’ll gain the skills to design and build end-to-end AI solutions with LlamaIndex, including RAG, structured data extraction, and multi-agent workflows in any domain.

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