RAG Full Course in 10 Hours | Complete Tutorial + Real-World Projects | Euron

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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

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