Udemy - Complete RAG Bootcamp - Build, Optimize, and Deploy AI Ap...
- Category Other
- Type Tutorials
- Language English
- Total size 4.0 GB
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Infohash : 03D0765EF4C5FEE7D55E04BE36FF4BAAFD486FDF
Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps
https://WebToolTip.com
Published 10/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 6h 31m | Size: 3.97 GB
Learn to build intelligent, retrieval-powered AI systems using LangChain, LlamaIndex, and real-world RAG workflows
What you'll learn
Design and Build a Retrieval-Augmented Generation (RAG) System Understand how to integrate large language models (LLMs) with retrieval pipelines
Implement Embeddings and Vector Databases for Semantic Search Learn how to generate and store embeddings using tools like OpenAI, ChromaDB, or Pinecone
Develop an End-to-End AI Knowledge Assistant Build and deploy a functional AI chatbot using frameworks like LangChain, Streamlit, and FastAPI
Evaluate and Optimize AI Performance Metrics Measure your assistant’s accuracy, relevance, and user experience using key performance metrics
Requirements
Basic Python Programming Skills Familiarity with Python syntax and libraries (like pandas, requests, or json) will make it easier to follow along with code nstrations.
Curiosity About AI and LLMs A foundational understanding of how Large Language Models (LLMs) like ChatGPT or Llama work conceptually will be helpful, but not mandatory — everything is explained in simple terms.
Access to a Computer with Internet You’ll need a computer capable of running Python and Jupyter notebooks or VS Code, plus an internet connection to install packages and access APIs.
Free or Trial Accounts for Tools Some hands-on labs will use free-tier APIs or tools such as OpenAI, LangChain, ChromaDB, and Streamlit — setup instructions are provided in the course.
Files:
[ WebToolTip.com ] Udemy - Complete RAG Bootcamp - Build, Optimize, and Deploy AI Apps- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Introduction to Retrieval-Augmented Generation
- 1 - 1.1 What is RAG.en_US.vtt (10.7 KB)
- 1 - 1.1 What is RAG.mp4 (109.4 MB)
- 2 - 1.2 RAG System Overview.en_US.vtt (8.9 KB)
- 2 - 1.2 RAG System Overview.mp4 (88.7 MB)
- 3 - 1.3 Applications of RAG.en_US.vtt (11.0 KB)
- 3 - 1.3 Applications of RAG.mp4 (113.8 MB)
- 4 - Hands on Lab.html (4.7 KB)
- 1 - 2.1 The Retrieval Process.en_US.vtt (10.0 KB)
- 1 - 2.1 The Retrieval Process.mp4 (107.3 MB)
- 2 - 2.2 The Generation Process.en_US.vtt (9.2 KB)
- 2 - 2.2 The Generation Process.mp4 (95.0 MB)
- 3 - 2.3 Putting It Together.en_US.vtt (13.2 KB)
- 3 - 2.3 Putting It Together.mp4 (142.5 MB)
- 4 - Hands on Lab.html (4.9 KB)
- 1 - 3.1 What Are Embeddings.en_US.vtt (12.5 KB)
- 1 - 3.1 What Are Embeddings.mp4 (125.3 MB)
- 2 - 3.2 Introduction to Vector Databases.en_US.vtt (13.4 KB)
- 2 - 3.2 Introduction to Vector Databases.mp4 (151.8 MB)
- 3 - 3.3 Building and Querying Vector Stores.en_US.vtt (12.3 KB)
- 3 - 3.3 Building and Querying Vector Stores.mp4 (131.0 MB)
- 4 - Hands on Lab.html (6.1 KB)
- 1 - 4.1 LangChain Core Concepts.en_US.vtt (12.3 KB)
- 1 - 4.1 LangChain Core Concepts.mp4 (124.6 MB)
- 2 - 4.2 RAG Implementation with LangChain.en_US.vtt (11.5 KB)
- 2 - 4.2 RAG Implementation with LangChain.mp4 (112.0 MB)
- 3 - 4.3 Adding Context and Metadata.en_US.vtt (10.6 KB)
- 3 - 4.3 Adding Context and Metadata.mp4 (109.3 MB)
- 4 - Hands on Lab.html (6.3 KB)
- 1 - 5.1 Advanced Retrieval Techniques.en_US.vtt (11.1 KB)
- 1 - 5.1 Advanced Retrieval Techniques.mp4 (117.0 MB)
- 2 - 5.2 Optimizing Context and Prompts.en_US.vtt (11.8 KB)
- 2 - 5.2 Optimizing Context and Prompts.mp4 (115.1 MB)
- 3 - 5.3 Evaluation and Metrics.en_US.vtt (12.9 KB)
- 3 - 5.3 Evaluation and Metrics.mp4 (141.6 MB)
- 4 - Hands on Lab.html (7.3 KB)
- 1 - 6.1 Front-End Integration.en_US.vtt (15.0 KB)
- 1 - 6.1 Front-End Integration.mp4 (159.2 MB)
- 2 - 6.2 Backend APIs.en_US.vtt (14.9 KB)
- 2 - 6.2 Backend APIs.mp4 (160.1 MB)
- 3 - 6.3 Deployment & Scalability.en_US.vtt (14.8 KB)
- 3 - 6.3 Deployment & Scalability.mp4 (162.6 MB)
- 4 - Hands on Lab.html (6.9 KB)
- 1 - 7.1 Hybrid Search (Keyword + Vector).en_US.vtt (15.8 KB)
- 1 - 7.1 Hybrid Search (Keyword + Vector).mp4 (149.5 MB)
- 2 - 7.2 Multi-Modal RAG.en_US.vtt (24.2 KB)
- 2 - 7.2 Multi-Modal RAG.mp4 (241.2 MB)
- 3 - 7.3 Agentic RAG.en_US.vtt (24.6 KB)
- 3 - 7.3 Agentic RAG.mp4 (243.9 MB)
- 4 - Hands on Lab.html (8.3 KB)
- 1 - 8.1 Enterprise & Industry RAG Solutions.en_US.vtt (24.2 KB)
- 1 - 8.1 Enterprise & Industry RAG Solutions.mp4 (238.4 MB)
- 2 - 8.2 Security & Governance.en_US.vtt (24.2 KB)
- 2 - 8.2 Security & Governance.mp4 (257.1 MB)
- 3 - 8.3 Integrating RAG into Workflows.en_US.vtt (23.5 KB)
- 3 - 8.3 Integrating RAG into Workflows.mp4 (231.6 MB)
- 4 - Hands on Lab.html (7.4 KB)
- 1 - 9.1 RAG for Developers & Data Scientists.en_US.vtt (21.6 KB)
- 1 - 9.1 RAG for Developers & Data Scientists.mp4 (200.3 MB)
- 2 - 9.2 Capstone Project.en_US.vtt (24.7 KB)
- 2 - 9.2 Capstone Project.mp4 (243.2 MB)
- Bonus Resources.txt (0.1 KB)
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