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