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Fundamentals of RAG

Master Retrieval-Augmented Generation with our hands-on Fundamentals of RAG Course!
Jeremy Morgan
Innovative Tech Leader, Linux Expert, & Educator
DevOps Pre-Requisite Course
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Description

Fundamentals of RAG Course: Master Retrieval-Augmented Generation for DevOps and AI

Unlock your expertise in Retrieval-Augmented Generation (RAG) with this comprehensive, hands-on online course designed for developers, DevOps engineers, and professionals looking to master RAG and intelligent document retrieval. The “Fundamentals of RAG” course guides you step-by-step from foundational concepts to building and deploying fully functional RAG pipelines on modern platforms.

Module 1: Introduction to RAG and DevOps Applications

Start your journey by understanding what RAG is, why it is transforming DevOps and AI workflows, and how it compares to alternative architectures. Through practical demonstrations, you’ll set up Ollama on various platforms (including Apple Silicon and NVIDIA GPU), structure real-world projects, and configure your development environment for immediate productivity.

Module 2: Document Processing and Chunking Strategies

Master the crucial skill of ingesting and processing documents in various formats—including text, DOCX, PDF, and CSV. Learn both basic and advanced chunking strategies to prepare your data for high-performance retrieval, with interactive labs that ensure you can apply these techniques in production environments.

Module 3: Keyword Search and Retrieval Algorithms

Dive into the fundamentals of keyword-based search and retrieval. This module covers reranking, TF-IDF, and the widely recognized BM25 algorithm, giving you the building blocks to implement and optimize effective search solutions within your RAG applications.

Module 4: Semantic Search and Embedding Models

Explore the limitations of traditional keyword methods and unlock the power of semantic search using modern embedding models. Get hands-on with sentence transformers and similarity calculations, learning to build intelligent, meaning-based search systems that go far beyond keywords.

Module 5: Vector Databases for RAG

Understand the vital role of vector databases in RAG system performance. Compare different vector database options and gain practical experience with ChromaDB implementation. This module equips you to manage large-scale, high-dimensional data for enterprise-grade RAG use cases.

Module 6: Building End-to-End RAG Pipelines

Bring all your learning together as you design, build, and deploy an end-to-end RAG pipeline. Learn about RAG architecture, seamless pipeline building, integration with existing systems, caching strategies, and advanced monitoring for reliability, scalability, and observability.

Key Features:

  • Comprehensive coverage of Retrieval-Augmented Generation (RAG)
  • Practical labs and real-world implementation examples
  • Guidance on document ingestion, chunking, search algorithms, and semantic search
  • Vector database fundamentals, including ChromaDB
  • Integration, caching, and monitoring for robust production RAG systems
  • Perfect for DevOps engineers, AI developers, and technology leaders

Enroll now in the Fundamentals of RAG course and become a leader in next-generation AI-powered search, DevOps automation, and intelligent data retrieval. Take the first step towards building innovative, future-ready applications!

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What our students say

About the instructor

Jeremy Morgan is a Senior Training Architect with endless enthusiasm for learning and sharing knowledge. Since transitioning from an engineering practitioner to an instructor in 2019, he has been dedicated to helping others excel. Passionate about DevOps, Linux, Machine Learning, and Generative AI, Jeremy actively shares his expertise through videos, articles, talks, and his tech blog, which attracts 9,000 daily readers. His work has been featured on Lifehacker, Wired, Hacker News, and Reddit.

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Introduction

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11
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Lesson Content

Module Content

Course Introduction 02:26
GitHub Repository for RAG Course
What is RAG and Why DevOps Teams Need It 10:16
RAG Architecture Deep Dive 11:33
When to Use RAG vs Alternatives 03:47
Demo: Ollama Setup and Walkthrough 07:02
Demo: Connecting with Ollama 03:38
Demo: Apple Silicon and Ollama 05:13
Demo: NVIDIA Architecture and Ollama 03:24
Demo: Project Structure and Development Environment 05:39
How to Reach Out to KodeKloud and Engage with the Community

Document Processing and Chunking

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Module Content

Document Ingestion Fundamentals 03:04
Demo: Ingesting Text 08:25
Demo: Ingesting Docx 07:46
Demo: Ingesting PDF 09:47
Demo: Ingesting CSV 07:48
Chunking Strategies Explained 19:42
Demo: Advanced Chunking Strategies 08:47
Lab: Document Chunking

Keyword Search & Retrieval

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Module Content

Introduction to Retrieval Methods 10:23
Reranking 04:28
TF-IDF Algorithm 10:23
BM25 Algorithm 07:30
Demo: Keyword search vs Semantic search 08:27

Semantic Search & Embeddings

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Module Content

Limitations of Keyword Search 15:47
Understanding Embedding Models 10:05
Sentence Transformers 11:01
Similarity Calculations 10:59
Demo: BM25 vs Semantic Search 21:33
Demo: From Keyword to Semantic Search 06:12
Lab: RAG Search Methods

Vector Databases

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Why Vector Databases? 08:16
Vector Database Options for RAG 15:54
Demo: ChromaDB Implementation 14:04
Lab: VectorDB
Lab: Embedding Models

Building the RAG Pipeline

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Module Content

Demo: Hello RAG 15:00
Demo: Ingesting Local Files 14:18
Demo: Hybrid Retrieval (BM25 + Chroma + Ollama) 23:44
Demo: Testing your RAG System 18:01
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