RAG Mastery Build AI Apps with Your Own DataPublished 9/2026
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English |
Duration: 7h 12m |
Size: 4.98 GB
Learn Retrieval-Augmented Generation (RAG), LLM Apps, Vector Databases, Embeddings, and AI Search Systems from Zero
Understand how Retrieval-Augmented Generation (RAG) works and why it is used to build modern AI applications
Build a strong foundation in LLMs, embeddings, vector databases, semantic search, and AI application architecture
Design complete RAG pipelines that connect your own documents and data with Large Language Models
Learn document processing, chunking strategies, retrieval techniques, and methods for improving search quality
Evaluate and optimize RAG systems using metrics, testing datasets, and production improvement workflows
Understand advanced retrieval techniques including hybrid search, reranking, filtering, and query enhancement
Build knowledge of production-ready RAG systems including scalability, monitoring, security, and cost optimization
Apply RAG concepts to real-world AI applications such as enterprise assistants, document search, and intelligent chatbots
RequirementsNo previous knowledge of Retrieval-Augmented Generation (RAG) or Large Language Models is required. Basic programming knowledge is helpful but not mandatory. You should have curiosity about AI applications and a willingness to learn modern AI technologies.
DescriptionThis course contains the use of artificial intelligence.
Artificial Intelligence applications are moving beyond simple chatbots. Modern AI systems need access to private knowledge, company documents, and specialized information. Retrieval-Augmented Generation (RAG) is the technology that enables Large Language Models (LLMs) to answer questions using your own data.
In this comprehensive course, you will learn how to design, build, evaluate, and improve real-world RAG applications from the ground up.
We start by understanding the foundations of Large Language Models, AI applications, embeddings, vector databases, and semantic search. You will learn how RAG works internally and why it has become one of the most important architectures for building reliable AI systems.
Throughout the course, you will explore the complete RAG pipeline
- Preparing and processing documents
- Creating embeddings and understanding vector representations
- Building efficient retrieval systems
- Working with vector databases
- Designing RAG architectures
- Improving retrieval quality with advanced techniques
- Evaluating RAG performance
- Reducing hallucinations
- Optimizing accuracy, speed, and cost
- Designing production-ready AI applications
You will also learn practical engineering concepts used in real-world AI systems, including hybrid search, reranking, metadata filtering, evaluation workflows, monitoring, and production optimization.
By the end of this course, you will have a strong understanding of how modern AI applications are built and how to create RAG-powered solutions that can work with your own documents and business data.
This course is designed for developers, AI enthusiasts, software engineers, and anyone who wants to understand and build the next generation of intelligent applications.
No previous RAG experience is required. We will start from the fundamentals and gradually move toward advanced production concepts.
Who this course is forDevelopers and software engineers who want to build AI applications using Large Language Models and their own data
AI engineers and machine learning practitioners who want to understand and implement Retrieval-Augmented Generation systems
Beginners who want to learn how modern AI applications, embeddings, vector databases, and LLM systems work
Professionals who want to create enterprise AI assistants, document search systems, and knowledge-based chatbots
Anyone interested in learning one of the most important technologies behind modern AI applications
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