Basic To Advanced: Retreival-Augmented Generation (Rag)

Multi-modal RAG Stack: A Hands-on Journey Through Vector Stores, LLM Integration, and Advanced Retrieval Methods

What you'll learn

Build three professional-grade chatbots: Website, SQL, and Multimedia PDF

Master RAG architecture and implementation from fundamentals to advanced techniques

Run and optimize both open-source and commercial LLMs

Implement vector databases and embeddings for efficient information retrieval

Create sophisticated AI applications using LangChain framework

Deploy advanced techniques like prompt caching and query expansion

Understand how to deploy on AWS EC2 (Basic Guide)

Requirements

Basic Python knowledge is Good to have but not mandatory.

Description

Transform your development skills with our comprehensive course on Retrieval-Augmented Generation (RAG) and LangChain. Whether you're a developer looking to break into AI or an experienced programmer wanting to master RAG, this course provides the perfect blend of theory and hands-on practice to help you build production-ready AI applications.What You'll LearnBuild three professional-grade chatbots: Website, SQL, and Multimedia PDFMaster RAG architecture and implementation from fundamentals to advanced techniquesRun and optimize both open-source and commercial LLMsImplement vector databases and embeddings for efficient information retrievalCreate sophisticated AI applications using LangChain frameworkDeploy advanced techniques like prompt caching and query expansionCourse ContentSection 1: RAG FundamentalsUnderstanding Retrieval-Augmented Generation architectureCore components and workflow of RAG systemsBest practices for RAG implementationReal-world applications and use casesSection 2: Large Language Models (LLMs) - Hands-on PracticeSetting up and running open-source LLMs with OllamaModel selection and optimization techniquesPerformance tuning and resource managementPractical exercises with local LLM deploymentSection 3: Vector Databases & EmbeddingsDeep dive into embedding models and their applicationsHands-on implementation of FAISS, ANNOY, and HNSW methodsSpeed vs. accuracy optimization strategiesIntegration with Pinecone managed databasePractical vector visualization and analysisSection 4: LangChain FrameworkText chunking strategies and optimizationLangChain architecture and componentsAdvanced chain composition techniquesIntegration with vector stores and LLMsHands-on exercises with real-world dataSection 5: Advanced RAG TechniquesQuery expansion and optimizationResult re-ranking strategiesPrompt caching implementationPerformance optimization techniquesAdvanced indexing methodsSection 6: Building Production-Ready ChatbotsWebsite ChatbotArchitecture and implementationContent indexing and retrievalResponse generation and optimizationSQL ChatbotNatural language to SQL conversionQuery optimization and safetyDatabase integration best practicesMultimedia PDF ChatbotMulti-modal content processingPDF parsing and indexingRich media response generationWho This Course is ForSoftware developers looking to specialize in AI applicationsAI engineers wanting to master RAG implementationBackend developers interested in building intelligent chatbotsTechnical professionals seeking hands-on LLM experiencePrerequisitesBasic Python programming knowledgeFamiliarity with REST APIsUnderstanding of basic database conceptsBasic understanding of machine learning concepts (helpful but not required)Why Take This CourseIndustry-relevant skills currently in high demandHands-on experience with real-world examplesPractical implementation using Tesla Motors databaseComplete coverage from fundamentals to advanced conceptsProduction-ready code and best practicesWorkshop-tested content with proven resultsWhat You'll BuildBy the end of this course, you'll have built three professional-grade chatbots and gained practical experience with:RAG system implementationVector database integrationLLM optimizationAdvanced retrieval techniquesProduction-ready AI applicationsJoin us on this exciting journey to master RAG and LangChain, and position yourself at the forefront of AI development.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Outline

Section 2: RAG Fundamentals

Lecture 3 Section Intro

Lecture 4 Intro to RAG & Core Concepts

Lecture 5 Principles, Traditional Methods vs RAG

Lecture 6 Real-world applications and use cases

Lecture 7 Understanding Retrieval-Augmented Generation architecture

Section 3: Introduction to Large Language Models (LLMs)

Lecture 8 Section Intro

Lecture 9 Basics of LLMs and Closed Source Models

Lecture 10 Closed Source & Open Source LLMs (Continued)

Lecture 11 Closed vs Open Source Models & Software

Lecture 12 What does Retrieval-Augmented Generation (RAG) do to LLMs?

Lecture 13 Let's run an Open Source LLM locally!

Section 4: VS Code & Github Repo Setup

Lecture 14 Downloading Python, VS Code, Git and more

Lecture 15 Cloning and accessing all Projects

Section 5: Vector Databases & Embeddings

Lecture 16 Section Intro

Lecture 17 What are Vectors and Why we use them?

Lecture 18 What are Embeddings?

Lecture 19 Setting up over VS Code Project

Lecture 20 Audio, Graph, Text and Image Vectors & Embeddings

Lecture 21 Vector DB Indexing and Pinecone Setup

Lecture 22 Image, Text and Paragraph Indexing and Matching

Section 6: LangChain Framework & Building a Simple RAG Pipeline

Lecture 23 Section Intro

Lecture 24 Components of Basic RAG Pipeline, LangChain and Loaders

Lecture 25 Create a Website Chatbot

Lecture 26 Add a Memory to your Website Chatbot

Lecture 27 Building a CSV / Excel Data Chatbot

Section 7: LangChain / RAG Advanced

Lecture 28 Section Intro

Lecture 29 Advanced Text Splitting, Re-ranking, Chunking Techniques

Lecture 30 Building Query Expansion Workflow

Section 8: Advanced Projects with LangChain

Lecture 31 Section Intro

Lecture 32 SQL / Database Chatbot using LangChain

Lecture 33 Prompt Caching (In Memory and DB)

Lecture 34 Multi-modal Chatbot

Section 9: Completion!

Lecture 35 Congratulations!

Software developers looking to specialize in AI applications,AI engineers wanting to master RAG implementation,Backend developers interested in building intelligent chatbots,Technical professionals seeking hands-on LLM experience,Software Engineers Data Scientists, AI Engineers, Machine Learning Engineers