RAG Full Course in 10 Hours | Complete Tutorial + Real-World Projects | Euron
About this course
Euron - https://euron.one/ Course Link : https://euron.one/course/rag-masters For any queries or counseling, feel free to call or WhatsApp us at: +919110665931 / +919019065931 Step into the world of Retrieval-Augmented Generation (RAG) pipelines with this complete guide! This video breaks down RA...
Euron - https://euron.one/ Course Link : https://euron.one/course/rag-masters For any queries or counseling, feel free to call or WhatsApp us at: +919110665931 / +919019065931 Step into the world of Retrieval-Augmented Generation (RAG) pipelines with this complete guide! This video breaks down RAG concepts into easy-to-follow steps while showing you how to build real, working pipelines. Whether you’re just starting your AI journey or looking to strengthen your expertise in retrieval-based systems, this video is packed with practical insights and hands-on coding. What you’ll discover in this tutorial: - RAG Fundamentals: Learn how RAG works, from architecture to workflow. - Tools in Action: Explore vector databases, embeddings, LangChain, and other essentials. - Hands-On Projects: Apply RAG to solve real-world use cases. - Prompt Engineering: Learn how to optimize responses and handle private datasets. - Deployment Made Simple: Deploy RAG apps using Streamlit, Render, or AWS Elastic Beanstalk. Why this video is worth your time: - Beginner-friendly yet detailed enough for intermediate learners. - Hands-on coding and project-based learning. - Clear, structured explanations to help you actually build and deploy a RAG system. CHAPTERS: 00:00 - Announcements 01:36 - What is Retrieval Augmented Generation (RAG) 05:09 - How RAG Works 10:47 - Understanding Retrieval Augmented Generation 14:20 - Problems Solved by RAG 21:13 - Overview of RAG Pipeline 22:58 - RAG Pipeline Explained 27:56 - Generating Embeddings 38:35 - Preparing Your Own Data for RAG 41:02 - Creating Text Files for Data 49:53 - Creating Embeddings from Data 1:11:40 - Querying from Vector Database 1:14:47 - Final Operation: How RA Works 1:31:56 - Deploying Code in Streamlit 1:32:51 - Setting Up Application Directory 1:33:14 - Creating app.py File 1:37:00 - Developing app.py 1:41:34 - Creating Environment for Streamlit 1:46:28 - Testing Streamlit Application 1:50:04 - Deploying Application on Streamlit 1:50:34 - Deploying Application on Render 1:50:40 - Deploying Application on AWS Elastic Beanstalk 2:00:24 - Streamlit Deployment Hands-On 2:29:54 - Introduction to Document Loading 2:32:41 - Text Loader Overview 2:35:21 - Loading CSV Files 2:36:00 - Loading PDF Files 2:46:29 - Chunking and Splitting Data 3:05:14 - Lecture 2 Overview 3:10:28 - Cosine Similarity and Normalization 3:19:59 - Practical Cosine Similarity 3:26:01 - Introduction to Vector Databases 3:31:52 - Understanding Vector Representation 3:37:19 - Cosine Similarity Explained 4:11:28 - Step 2: Creating Embeddings 4:18:25 - Step 3: Creating Embedding Arrays 4:40:29 - Inserting Data into ChromaDB 4:43:14 - Querying ChromaDB 4:46:38 - Updating Records in ChromaDB 4:49:24 - Adding Metadata Information 4:56:22 - Persisting Collections in ChromaDB 5:03:20 - Pinecone Insert Operations 5:31:16 - Connecting to BayesVector 6:13:20 - Lecture 2: End to End ALM Chain 6:18:44 - Project Setup Process 6:24:08 - System Setup for ALM Chain 6:25:36 - Accessing LM and Embeddings 7:10:07 - Multi-Agent System with Self-Routing 7:12:05 - Accessing LLM in ALM 7:19:55 - Creating a Tool for ALM 7:22:08 - Creating an Agent in ALM 7:23:41 - Creating a Routing Agent 7:34:01 - Introduction to (LCEL) 8:03:02 - Setting the Entry Point in ALM 8:10:33 - Multi-Agent System Overview 8:16:08 - Creating Context Files 8:18:37 - Researcher Node in ALM 8:24:45 - Synthesizer Node Overview 8:27:20 - Classifier Node in ALM 8:28:45 - Finalizer Node Overview 8:42:01 - Understanding Prompting Techniques 8:43:20 - Crafting Effective Prompts 8:53:20 - Few-Shot Prompting Techniques 9:00:48 - Output Format Instructions 9:04:27 - Chain of Thought (COT) Prompting 9:09:24 - Explicit Anchoring Techniques 9:50:37 - Project Setup Process 9:54:41 - Obtaining URI API Key 9:57:48 - Storing and Retrieving Vectors 10:43:58 - Deploying the Chatbot Application 10:45:58 - Testing the Deployed Chatbot Roadmap for you : AI /Data Science Pro Level Expert Roadmap - https://euron.one/roadmap/c9361831-c806-45e2-b65c-c3f4c6cd2fa4 NLP expert Roadmap - https://euron.one/roadmap/300bc526-ed55-42e3-9072-43aca6c3ba4f Data Analytics / Business Analytics Expert Roadmap - https://euron.one/roadmap/920278e8-e3c0-4763-a135-ebed66074853 Big Data / Data Engineering Expert Roadmap - https://euron.one/roadmap/98c8db49-2eab-44b7-8fba-7f2b2575ec83 Computer Vision Roadmap - https://euron.one/roadmap/d8281277-5cfd-4498-bbff-4135aa178897 Deep Learning Roadmap - https://euron.one/roadmap/1495a7ba-4297-4cc9-8d68-5460cafb90ca Generative AI Roadmap - https://euron.one/roadmap/2380f611-7475-4343-b7f7-22b765710604 Machine Learning Expert Roadmap - https://euron.one/roadmap/ff514391-328e-4863-b810-0a5c5db6a170 Android- https://play.google.com/store/apps/details?id=com.euron.one&hl=en IOS - https://apps.apple.com/in/app/euron-your-learning-app/id6741360597
What learners say
AI summaryLearners find this course to be a comprehensive and high-quality resource for learning RAG, with many describing it as a masterclass and praising its real-world projects.
What learners praise
- comprehensive 10-hour coverage
- real-world projects
- masterclass quality
Common caveats
- module import issues in end-to-end projects
- unclear best approach for structured data like Excel
- need for guidance on handling long PDFs with cross-page relations
AI-generated from 9 viewer comments on YouTube — it summarizes outside comments and is not a CourseShelf review.
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