What will you learn in Mastering LlamaIndex: From Fundamentals to Building AI Apps Course
Understand LlamaIndex’s core architecture and how it connects unstructured data to LLMs.
Integrate any large language model with LlamaIndex for enhanced retrieval and query handling.
Design and implement a retrieval-augmented generation (RAG) pipeline for efficient information retrieval.
Extract structured data from unstructured text using schema-based techniques.
Build single-agent and multi-agent AI systems with memory, workflows, and coordination.
Program Overview
Module 1: Getting Started
⏳ 5 minutes
Topics: Course structure, tools, and foundational concepts.
Hands-on: Explore the learning environment and initial setup.
Module 2: Core Concepts and Using LLMs
⏳ 10 minutes
Topics: Fundamentals of LlamaIndex and LLM integration.
Hands-on: Connect to an LLM and perform simple queries.
Module 3: Building a RAG Pipeline
⏳ 7 minutes
Topics: Retrieval-augmented generation architecture.
Hands-on: Implement a basic RAG workflow with LlamaIndex.
Module 4: Extracting Structured Outputs from LLMs
⏳ 7 minutes
Topics: Schema-based data extraction techniques.
Hands-on: Define and apply a schema to parse unstructured text.
Module 5: Agents and Workflows
⏳ 15 minutes
Topics: Building single-agent and multi-agent systems with memory.
Hands-on: Create an AI agent pipeline with shared state and decision logic.
Module 6: Monitoring and Evaluating LLM Applications
⏳ 8 minutes
Topics: Tracing, debugging, and performance evaluation.
Hands-on: Instrument a workflow and assess reliability metrics.
Module 7: Building Real-World Applications with LlamaIndex
⏳ 10 minutes
Topics: End-to-end project implementations (Q&A system, resume optimizer, lesson-plan generator).
Hands-on: Assemble and deploy a multi-turn document Q&A system.
Module 8: Wrap Up
⏳ 5 minutes
Topics: Key takeaways and next steps.
Hands-on: Review and plan your own LlamaIndex project.
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Job Outlook
Companies building AI-driven products seek engineers who can architect RAG systems and AI agents.
Roles include AI Engineer, NLP Developer, and ML Infrastructure Specialist with LLM integration expertise.
Salaries range from $100K–$150K+ in major tech hubs for professionals skilled in LlamaIndex, RAG, and agent workflows.
Knowledge of monitoring, schema extraction, and multi-agent orchestration is increasingly valuable in enterprise AI and automation.
Specification: Mastering LlamaIndex: From Fundamentals to Building AI Apps
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