RAG: Beyond Basics

Name: RAG: Beyond Basics
Price: 9 USD
Availability: InStock

2h 40m 48s

English

Paid

October 15, 2025

Course description

The course is dedicated to the practical and theoretical study of Retrieval-Augmented Generation (RAG). You will learn not only "how" but also "why" these methods work, and you will also learn how to create reliable "chat with documents" applications using modern LLMs and advanced RAG techniques.

The program includes building a basic pipeline, moving to advanced strategies like re-ranking and query expansion, and working with both commercial and local models. The course combines theory with practical programming in Python and the use of tools such as LangChain and Streamlit.

The course is suitable for developers, SaaS product founders, and managers who need to quickly extract value from large volumes of text information. By the end of the course, you will have your own functioning RAG pipeline and a deep understanding of approaches that allow you to take applications to a new level of performance.

Watch Online

0:00

#1: What is RAG? Why we NEED it?

All Course Lessons (27)

#	Lesson Title	Duration
1	What is RAG? Why we NEED it? Demo	04:59
2	Setting up Virtual Environment	04:04
3	Setting Up API Keys	03:52
4	Deep Dive into RAG Pipeline Structure	04:01
5	Demystifying Embedding Models and Vector Storage	06:18
6	Google Colab Setup	03:10
7	End-to-End RAG Pipeline - Code Time	02:11
8	Loading and Processing PDF Files	02:36
9	How Chunking Works	06:49
10	Focus on Parsing than Chunking	02:07
11	Chunk Size as Function of Text Embedding Models	05:28
12	The Retrieval in RAG	04:38
13	Putting Everything Together - 1st Iteration of RAG	05:13
14	RAG: Advanced Techniques	01:13
15	Improving RAG with Re-ranking for Precise Information Retrieval - Part 1	06:41
16	Re-Ranking with GPT-4, ColBERT, and Cohere	07:32
17	Improving Information Retrieval with Query Expansion using LLMs	08:14
18	Enhancing Search with Hypothetical Documents Embedding Technique	08:02
19	Enhancing Document Retrieval with Ensemble Techniques	06:55
20	Hierarchical Chunking - Exploring the Parent Document Retriever	08:25
21	From Notebook to working Scripts	12:05
22	Creating Streamlit UI App	05:02
23	Private and local Chat with PDFs	04:43
24	The Recap	03:58
25	Contextual Retrieval - Adding Context to Your Chunks	09:31
26	Contextual Retrieval - Implementation	09:26
27	Multimodal RAG - Working with Images and Tables	13:35