Building Data-Driven Applications with LlamaIndex
A practical guide to RAG pipelines, agentic workflows, and production AI deployment
Created by Andrei Gheorghiu
Discover how to build reliable, data-driven AI applications using LlamaIndex. You will learn to connect large language models with your own data, design advanced retrieval strategies, and implement agentic workflows for robust, production-ready solutions.
Packt | May 2026 | 640 min
What You Will Learn
You will gain practical experience by building an interactive web application step by step. Each section introduces new concepts and techniques, guiding you through data ingestion, retrieval strategies, agentic workflows, and deployment. Real-world code examples and clear explanations help you apply what you learn immediately.
Key Features
- Connect language models to external data for more accurate, up-to-date responses
- Design agentic workflows and multi-agent systems for complex AI tasks
- Deploy and evaluate AI-powered web apps using Python and Streamlit
Target Audience
Ideal for Python developers and AI practitioners with basic knowledge of large language models. If you want to create interactive, generative AI applications that use your own data and are ready to explore advanced RAG and agentic techniques, this course will help you reach your goals.
