Mastering LlamaIndex: From Fundamentals to Building AI Apps Course Syllabus

Overview: This concise, project-driven course guides you through LlamaIndex’s core capabilities, from foundational concepts to building production-ready AI applications. In under two hours, you’ll progress through hands-on modules covering RAG pipelines, structured data extraction, agent workflows, and real-world application deployment. Each module combines focused instruction with interactive labs, enabling you to quickly master tools used by leading AI engineering teams. Perfect for developers aiming to integrate LLMs into scalable, maintainable systems with real-time monitoring and evaluation.

Module 1: Getting Started

Estimated time: 0.1 hours

• Course structure and learning path
• Setting up the development environment
• Introduction to LlamaIndex tools and interface
• Hands-on exploration of the learning platform

Module 2: Core Concepts and Using LLMs

Estimated time: 0.2 hours

• Understanding LlamaIndex architecture
• Connecting to large language models (LLMs)
• Performing basic queries with LLM integration
• Handling unstructured data inputs

Module 3: Building a RAG Pipeline

Estimated time: 0.1 hours

• Retrieval-augmented generation (RAG) fundamentals
• Indexing documents for efficient retrieval
• Implementing a basic RAG workflow
• Querying with context-augmented responses

Module 4: Extracting Structured Outputs from LLMs

Estimated time: 0.1 hours

• Schema-based structured data extraction
• Defining output schemas for LLM parsing
• Applying structured extraction to unstructured text

Module 5: Agents and Workflows

Estimated time: 0.3 hours

• Designing single-agent systems with memory
• Orchestrating multi-agent workflows
• Implementing decision logic and state sharing
• Building coordinated AI agent pipelines

Module 6: Monitoring and Evaluating LLM Applications

Estimated time: 0.1 hours

• Tracing and debugging LLM workflows
• Instrumenting applications for observability
• Evaluating performance and reliability metrics

Module 7: Building Real-World Applications with LlamaIndex

Estimated time: 0.2 hours

• End-to-end Q&A system implementation
• Resume optimizer workflow
• Lesson-plan generation using LlamaIndex
• Deploying a multi-turn document Q&A system

Module 8: Wrap Up

Estimated time: 0.1 hours

• Key takeaways from the course
• Planning your own LlamaIndex project
• Next steps in mastering AI applications

Prerequisites

• Familiarity with Python programming
• Basic understanding of large language models (LLMs)
• Experience with command-line and development tools

What You'll Be Able to Do After

• Design and implement RAG pipelines for efficient information retrieval
• Extract structured data from unstructured text using schema-based techniques
• Build and coordinate single-agent and multi-agent AI systems
• Integrate and monitor LLM applications in production environments
• Deploy end-to-end AI applications like document Q&A and lesson-plan generators