Master Rag: Build Ai Systems With Llamaindex-2025

[mitsumi]

Master Rag: Build Ai Systems With Llamaindex-2025
Published 11/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 6h 16m | Size: 3.27 GB
Learn RAG, LlamaIndex, vector databases & advanced retrieval techniques to build smarter AI applications.


What you'll learn
Understand the core concepts of Retrieval-Augmented Generation and why RAG improves LLM accuracy and reliability.
Build complete RAG pipelines using LlamaIndex, including ingestion, indexing, querying, and retrieval optimization.
Work with vector databases like Qdrant and integrate external data sources into AI-powered applications.
Evaluate and optimize RAG systems using advanced techniques such as reranking, hybrid retrieval, and query transformation.
Requirements
Basic Python knowledge is helpful but not required-everything is taught step-by-step for beginners.
Description
Some parts of this course and its content were created or assisted using Artificial Intelligence tools to enhance clarity, structure, and learning efficiency.This course provides a complete and practical guide to mastering Retrieval-Augmented Generation (RAG) using the powerful LlamaIndex framework. Designed for developers, data scientists, and AI enthusiasts, this training will take you from the foundational concepts of retrieval systems to the advanced techniques required to build high-performance, knowledge-aware AI applications.You will begin by understanding the limitations of Large Language Models and why RAG has become essential for improving accuracy, reliability, and factual grounding. Through hands-on lessons, you will learn how to configure your environment with GitHub Codespaces, choose the right LLM provider, work with vector databases such as Qdrant, and upload and index your own datasets.The course then dives into the core architecture of LlamaIndex, covering ingestion pipelines, query pipelines, chunk optimization, metadata extraction, semantic segmentation, hybrid retrieval, and advanced evaluation techniques. You will also explore modular RAG design, post-retrieval enhancements, reranking strategies, and methods to build scalable AI systems capable of handling real-world data.By the end of this course, you will be able to design, build, optimize, and evaluate complete RAG systems from scratch. You will gain both theoretical understanding and practical experience, enabling you to create AI tools, chatbots, assistants, and enterprise-level applications that leverage external knowledge efficiently and intelligently.This is the ultimate 2025 guide to building cutting-edge RAG-powered AI systems with LlamaIndex.
Who this course is for
Developers, data scientists, and AI enthusiasts who want to build smarter, knowledge-enhanced AI applications using RAG.

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