RAG (Retrieval)
4h 33m 19s
English
Paid
Course description
Study the key principles of developing Retrieval-Augmented Generation (RAG) systems and the application of advanced search techniques to improve the performance of artificial intelligence. As part of the course, you will master the integration of RAG into AI applications, optimization of information retrieval and answer generation processes, and learn to apply advanced machine learning technologies to enhance the accuracy and efficiency of intelligent systems.
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| # | Title | Duration |
|---|---|---|
| 1 | 1.1 Intro | 09:32 |
| 2 | 1.2 RAG Overview | 12:58 |
| 3 | 1.3 Code Setup | 08:39 |
| 4 | 1.4 Embeddings | 12:08 |
| 5 | 1.5 Vector Databases | 20:42 |
| 6 | 1.6 Similarity Search | 08:12 |
| 7 | 2.1 Intro | 08:38 |
| 8 | 2.2 Vector DB Setup | 19:07 |
| 9 | 2.3 Generating Embeddings | 12:35 |
| 10 | 2.4 Uploading Data | 08:05 |
| 11 | 2.5 Basic Retrieval | 11:57 |
| 12 | 2.6 Query Optimization | 09:41 |
| 13 | 2.7 Document Reranking | 10:59 |
| 14 | 2.8 Metadata Filtering | 11:03 |
| 15 | 2.9 Text Splitting | 08:36 |
| 16 | 2.10 All Together | 09:27 |
| 17 | 2.11 RAG Prompting | 12:44 |
| 18 | 3.1 Project Intro | 09:19 |
| 19 | 3.2 Initialize Code | 06:50 |
| 20 | 3.3 Setup Vector DB | 12:03 |
| 21 | 3.4 Build RAG Pipeline | 27:48 |
| 22 | 3.5 Connect Frontend | 22:16 |
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