RAG: Beyond Basics
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.
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| # | Title | Duration |
|---|---|---|
| 1 | What is RAG? Why we NEED it? | 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 |
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