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Udemy - Complete RAG Bootcamp Build, Optimize, and Deploy AI Apps 2025-10

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File Name
Size
Readme.txt
148 B
1 - Introduction to Retrieval-Augmented Generation/4 - Hands on Lab.html
4.7 kB
2 - Foundations of RAG Architecture/4 - Hands on Lab.html
4.9 kB
3 - Working with Embeddings and Vector Databases/4 - Hands on Lab.html
6.1 kB
4 - Section 4 Building RAG Pipelines with LangChain/4 - Hands on Lab.html
6.3 kB
6 - Deploying RAG Systems/4 - Hands on Lab.html
6.9 kB
5 - Enhancing RAG Performance/4 - Hands on Lab.html
7.3 kB
8 - Real-World Use Cases/4 - Hands on Lab.html
7.4 kB
7 - Advanced & Hybrid RAG Techniques/4 - Hands on Lab.html
8.3 kB
1 - Introduction to Retrieval-Augmented Generation/2 - 1.2 RAG System Overview.en_US.vtt
8.9 kB
2 - Foundations of RAG Architecture/2 - 2.2 The Generation Process.en_US.vtt
9.2 kB
2 - Foundations of RAG Architecture/1 - 2.1 The Retrieval Process.en_US.vtt
10 kB
4 - Section 4 Building RAG Pipelines with LangChain/3 - 4.3 Adding Context and Metadata.en_US.vtt
11 kB
1 - Introduction to Retrieval-Augmented Generation/1 - 1.1 What is RAG.en_US.vtt
11 kB
1 - Introduction to Retrieval-Augmented Generation/3 - 1.3 Applications of RAG.en_US.vtt
11 kB
5 - Enhancing RAG Performance/1 - 5.1 Advanced Retrieval Techniques.en_US.vtt
11 kB
4 - Section 4 Building RAG Pipelines with LangChain/2 - 4.2 RAG Implementation with LangChain.en_US.vtt
12 kB
5 - Enhancing RAG Performance/2 - 5.2 Optimizing Context and Prompts.en_US.vtt
12 kB
4 - Section 4 Building RAG Pipelines with LangChain/1 - 4.1 LangChain Core Concepts.en_US.vtt
12 kB
3 - Working with Embeddings and Vector Databases/3 - 3.3 Building and Querying Vector Stores.en_US.vtt
12 kB
3 - Working with Embeddings and Vector Databases/1 - 3.1 What Are Embeddings.en_US.vtt
12 kB
5 - Enhancing RAG Performance/3 - 5.3 Evaluation and Metrics.en_US.vtt
13 kB
2 - Foundations of RAG Architecture/3 - 2.3 Putting It Together.en_US.vtt
13 kB
3 - Working with Embeddings and Vector Databases/2 - 3.2 Introduction to Vector Databases.en_US.vtt
13 kB
6 - Deploying RAG Systems/3 - 6.3 Deployment & Scalability.en_US.vtt
15 kB
6 - Deploying RAG Systems/2 - 6.2 Backend APIs.en_US.vtt
15 kB
6 - Deploying RAG Systems/1 - 6.1 Front-End Integration.en_US.vtt
15 kB
7 - Advanced & Hybrid RAG Techniques/1 - 7.1 Hybrid Search (Keyword + Vector).en_US.vtt
16 kB
9 - Section 9/1 - 9.1 RAG for Developers & Data Scientists.en_US.vtt
22 kB
8 - Real-World Use Cases/3 - 8.3 Integrating RAG into Workflows.en_US.vtt
24 kB
8 - Real-World Use Cases/1 - 8.1 Enterprise & Industry RAG Solutions.en_US.vtt
24 kB
8 - Real-World Use Cases/2 - 8.2 Security & Governance.en_US.vtt
24 kB
7 - Advanced & Hybrid RAG Techniques/2 - 7.2 Multi-Modal RAG.en_US.vtt
24 kB
7 - Advanced & Hybrid RAG Techniques/3 - 7.3 Agentic RAG.en_US.vtt
25 kB
9 - Section 9/2 - 9.2 Capstone Project.en_US.vtt
25 kB
1 - Introduction to Retrieval-Augmented Generation/2 - 1.2 RAG System Overview.mp4
89 MB
2 - Foundations of RAG Architecture/2 - 2.2 The Generation Process.mp4
95 MB
2 - Foundations of RAG Architecture/1 - 2.1 The Retrieval Process.mp4
107 MB
4 - Section 4 Building RAG Pipelines with LangChain/3 - 4.3 Adding Context and Metadata.mp4
109 MB
1 - Introduction to Retrieval-Augmented Generation/1 - 1.1 What is RAG.mp4
109 MB
4 - Section 4 Building RAG Pipelines with LangChain/2 - 4.2 RAG Implementation with LangChain.mp4
112 MB
1 - Introduction to Retrieval-Augmented Generation/3 - 1.3 Applications of RAG.mp4
114 MB
5 - Enhancing RAG Performance/2 - 5.2 Optimizing Context and Prompts.mp4
115 MB
5 - Enhancing RAG Performance/1 - 5.1 Advanced Retrieval Techniques.mp4
117 MB
4 - Section 4 Building RAG Pipelines with LangChain/1 - 4.1 LangChain Core Concepts.mp4
125 MB
3 - Working with Embeddings and Vector Databases/1 - 3.1 What Are Embeddings.mp4
125 MB
3 - Working with Embeddings and Vector Databases/3 - 3.3 Building and Querying Vector Stores.mp4
131 MB
5 - Enhancing RAG Performance/3 - 5.3 Evaluation and Metrics.mp4
142 MB
2 - Foundations of RAG Architecture/3 - 2.3 Putting It Together.mp4
142 MB
7 - Advanced & Hybrid RAG Techniques/1 - 7.1 Hybrid Search (Keyword + Vector).mp4
150 MB
3 - Working with Embeddings and Vector Databases/2 - 3.2 Introduction to Vector Databases.mp4
152 MB
6 - Deploying RAG Systems/1 - 6.1 Front-End Integration.mp4
159 MB
6 - Deploying RAG Systems/2 - 6.2 Backend APIs.mp4
160 MB
6 - Deploying RAG Systems/3 - 6.3 Deployment & Scalability.mp4
163 MB
9 - Section 9/1 - 9.1 RAG for Developers & Data Scientists.mp4
200 MB
8 - Real-World Use Cases/3 - 8.3 Integrating RAG into Workflows.mp4
232 MB
8 - Real-World Use Cases/1 - 8.1 Enterprise & Industry RAG Solutions.mp4
238 MB
7 - Advanced & Hybrid RAG Techniques/2 - 7.2 Multi-Modal RAG.mp4
241 MB
9 - Section 9/2 - 9.2 Capstone Project.mp4
243 MB
7 - Advanced & Hybrid RAG Techniques/3 - 7.3 Agentic RAG.mp4
244 MB
8 - Real-World Use Cases/2 - 8.2 Security & Governance.mp4
257 MB