Master RAG- Ultimate Retrieval-Augmented Generation Course

Master RAG: Ultimate Retrieval-Augmented Generation Course, available at $109.99, has an average rating of 4.58, with 62 lectures, 3 quizzes, based on 19 reviews, and has 531 subscribers.

You will learn about Understand the Fundamentals of Retrieval-Augmented Generation (RAG) Explore advanced techniques to optimize and fine-tune the RAG pipeline Experiment with the levels of Text splitting (simple to complex) with examples to improve the retrieval process Learn to handle multiple document types to prep data for the LLM (unstructured(dot)io) Experiment with text splitters, Chunking strategies and optimization techniques Develop a comprehensive project: A multi-agent LLM-driven application using LangGraph Enhance RAG systems with pre-retrieval and post-retrieval optimization techniques and learn retrieval optimization with Query Transformation and Decomposition This course is ideal for individuals who are Python developers & ML Engineers who want to build AI-driven applications leveraging LLMs or Students and Learners willing to dive into RAG implementations and gain hands-on experience with practical examples or Tech Entrepreneurs and AI Enthusiasts seeking new learning and business opportunities in AI It is particularly useful for Python developers & ML Engineers who want to build AI-driven applications leveraging LLMs or Students and Learners willing to dive into RAG implementations and gain hands-on experience with practical examples or Tech Entrepreneurs and AI Enthusiasts seeking new learning and business opportunities in AI.

Enroll now: Master RAG: Ultimate Retrieval-Augmented Generation Course

Summary

Title: Master RAG: Ultimate Retrieval-Augmented Generation Course

Price: $109.99

Average Rating: 4.58

Number of Lectures: 62

Number of Quizzes: 3

Number of Published Lectures: 61

Number of Published Quizzes: 3

Number of Curriculum Items: 65

Number of Published Curriculum Objects: 64

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the Fundamentals of Retrieval-Augmented Generation (RAG)

Explore advanced techniques to optimize and fine-tune the RAG pipeline

Experiment with the levels of Text splitting (simple to complex) with examples to improve the retrieval process

Learn to handle multiple document types to prep data for the LLM (unstructured(dot)io)

Experiment with text splitters, Chunking strategies and optimization techniques

Develop a comprehensive project: A multi-agent LLM-driven application using LangGraph

Enhance RAG systems with pre-retrieval and post-retrieval optimization techniques and learn retrieval optimization with Query Transformation and Decomposition

Who Should Attend

Python developers & ML Engineers who want to build AI-driven applications leveraging LLMs

Students and Learners willing to dive into RAG implementations and gain hands-on experience with practical examples

Tech Entrepreneurs and AI Enthusiasts seeking new learning and business opportunities in AI

Target Audiences

Python developers & ML Engineers who want to build AI-driven applications leveraging LLMs

Students and Learners willing to dive into RAG implementations and gain hands-on experience with practical examples

Tech Entrepreneurs and AI Enthusiasts seeking new learning and business opportunities in AI

Welcome to “Master RAG: Ultimate Retrieval-Augmented Generation Course”!

This course is a deep dive into the world of Retrieval-Augmented Generation (RAG) systems. If you aim to build powerful AI-driven applications and leverage language models, this course is for you! Perfect for anyone wanting to master the skills needed to develop intelligent retrieval-based applications.

This hands-on course will guide you through the core concepts of RAG architecture, explore various frameworks, and provide a thorough understanding and practical experience in building advanced RAG systems.

Enroll now and take the first step towards mastering RAG systems!

# What You’ll Learn:

Development of LLM-based applications: Understand the core concepts and capabilities of Large Language Models (LLMs) and explore high-level frameworks that facilitate powered by retrieval and generation tasks,

Optimizing and Scaling RAG Pipelines: Learn best practices for optimizing and scaling RAG pipelines using LangChain, including indexing, chunking, and retrieval optimization techniques,

Advanced RAG Techniques: Enhance RAG systems with pre-retrieval and post-retrieval optimization techniques and learn retrieval optimization with query transformation and decomposition,

Document Transformers and Chunking Strategies: Understand strategies for smart text division, handling large datasets, and improving document indexing and embeddings.

Debugging, Testing, and Monitoring LLM Applications: Use LangSmith to debug, test, and monitor LLM applications, evaluating each component of the RAG pipeline.

Building Multi-Agent LLM-Driven Applications: Develop complex stateful applications using LangGraph, making multiple agents collaborate on data retrieval and generation tasks.

Enhanced RAG Quality: Learn to process unstructured data, extract elements like tables and images from PDF files, and integrate GPT-4 Vision to identify and describe elements within images.

# What is Included?

1. Getting Started: Introduction and Setup

Python Development Environment Setup

Implement basic to advanced RAG pipelines

Quickstart: Building Your First LLM-Powered Application using OpenAI

Step-by-step OpenAI Guide to creating a basic application integrating the ChatOpenAI API for text and message generation

2. RAG: From Native (101) to Advanced RAG

Key benefits and limitations of using LLMs

Overview and understanding of the RAG pipeline and multiple use cases

Hands-on project: Implement a basic RAG Q&A system using LLMs, LangChain, and the FAISS vector database

[Project] – Build end-to-end RAG solutions using tools like FAISS and ChromaDB

3. Advanced RAG Techniques & Strategies

Enhance RAG systems with pre-retrieval and post-retrieval optimization techniques

Indexing and chunking optimization techniques

Retrieval optimization with query transformation and decomposition

4. Optimized RAG: Document Transformers & Chunking Strategies

Strategies for smart text division to handle large datasets and scaling applications

Improve document indexing and embeddings

Experiment with commonly used text splitters:

Split into chunks by characters with a fixed-size parameter

Split recursively by character

Semantic chunking with LangChain to split into sentences based on text similarity

5. LangSmith: Debug, Test, and Monitor LLM Applications

Evaluate each component of the RAG pipeline

Develop a comprehensive project: A multi-agent LLM-driven application using LangGraph

6. Enhanced RAG Quality: Conventional vs. Structured RAG

Learn to process unstructured data to facilitate integration and preparation for LLMs

Practice with a project aimed at extracting elements like tables and images from PDF files and integrating GPT-4 Vision to identify and describe elements within images

Bonus materials: Assessment questions, downloadable resources, interactive playgrounds (Google Colab)

# Who is This Course For?

Python Developers: Individuals who want to build AI-driven applications leveraging language models using high-level libraries and APIs

ML Engineers: Professionals looking to enhance their skills in RAG techniques

Students and Learners: Individuals eager to dive into the world of RAG systems and gain hands-on experience with practical examples

Tech Entrepreneurs and AI Enthusiasts: Anyone seeking to create intelligent, retrieval-based applications and explore new business opportunities in AI

Whether you’re a beginner or an advanced practitioner, this course will elevate your capabilities in constructing intelligent and efficient RAG pipelines with case studies and real-world examples.

This course offers a comprehensive guide through the main concepts of RAG architecture, providing a structured learning path from basic to advanced techniques, ensuring a robust understanding to gain practical experience in building LLM-powered apps.

Start your learning journey today and transform the way you develop retrieval-based applications!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Skills & Project requirements

Lecture 2: Development Environment Setup

Lecture 3: Download the Starter Project

Lecture 4: Integrate OpenAI into a Web Project (Quickstart)

Lecture 5: Integrate OpenAI into a Web Project (Quickstart) : send first API request

Chapter 2: RAG : from Native (101) to Advanced – Pre-Indexing, Re-Ranking, Summarization

Lecture 1: Introduction

Lecture 2: OpenAI Setup & Configuration : step-by-step Guide

Lecture 3: Starter project : Installation & Setup

Lecture 4: Retrieval QA Integration (FAISS)

Lecture 5: How to instantiate a ChatOpenAI model ?

Lecture 6: Retrieval QA integration : Retriever and Generate components

Lecture 7: Vector Stores (LangChain) & Embeddings explained

Lecture 8: The Main Building Blocks

Lecture 9: Build an End-2-End RAG Pipeline (ChromaDB)

Lecture 10: Split Documents into Chunks

Lecture 11: Build an End-2-End RAG Pipeline (ChromaDB) – Part 2

Lecture 12: Interactive playground (Google Colab) : Instructions

Lecture 13: Interactive playground (Google Colab): With or Without RAG

Lecture 14: Advanced Techniques to Enhance the RAG pipeline

Lecture 15: Download the Course Materials

Lecture 16: [Part 1/4]-Advanced RAG : Query Translation and Enhancement (Decomposition)

Lecture 17: [Part 2/4]-Advanced RAG : Query Decomposition and Enhancement – Answer queries

Lecture 18: [Part 3/4] – Advanced RAG : Query Decomposition and Enhancement – Optimized Answ

Lecture 19: [Part 4/4]-Advanced RAG : Query Decomposition and Enhancement

Chapter 3: Advanced RAG techniques & strategies

Lecture 1: Introduction

Lecture 2: Presentation & Setup

Lecture 3: [Part 1/2] – Advanced RAG : multi-querying, retrieve and consolidate results

Lecture 4: [Part 2/2] – Advanced RAG : multi-querying and generate accurate answers

Lecture 5: Advanced RAG : RAG-Fusion

Lecture 6: [Part 1/2] – Advanced RAG Fusion – multi-querying and reranking results

Lecture 7: [Part 2/2] – Advanced RAG Fusion – generate context-aware responses

Lecture 8: Advanced RAG : Corrective RAG (CRAG)

Lecture 9: [Part 1/4] – Advanced RAG : Corrective RAG

Lecture 10: [Part 2/4] – Advanced RAG : Corrective RAG – Retrieval Evaluator

Lecture 11: [Part 3/4] – Advanced RAG : Corrective RAG – Rewrite & web tool

Lecture 12: [Part 4/4] – Advanced RAG : Corrective RAG – generate response

Chapter 4: Optimized RAG : Document Transformers & Chunking Strategies

Lecture 1: Section intro : Smart Text Division with LangChain

Lecture 2: Level 1 – Split documents by Character vs. Recursively

Lecture 3: Understanding the CharacterTextSplitter Parameters (Online tool : ChunkViz)

Lecture 4: Level 2 – Split documents by character vs. recursively

Lecture 5: Levels 3 – Document specific splitting : split code and markup

Lecture 6: Levels 3 – Document-specific splitting : Code Splitting (Python)

Lecture 7: Levels 3 – Document-specific splitting : PDF (unstructured.io)

Lecture 8: Levels 3 – Document-specific splitting : extract and process elements from PDF d

Lecture 9: Other Types of TextSplitters

Lecture 10: Levels 4 & 5- Semantic Chunking (Embeddings-based) & Agentic approach

Chapter 5: LangSmith: Debug, Test, and Monitor LLM Applications

Lecture 1: Introduction

Lecture 2: RAG Implementation Tracing & Testing

Lecture 3: Integrating LangSmith into your workflow

Chapter 6: From Native, to Advanced to Agentic RAG (LangGraph)

Lecture 1: Introduction

Lecture 2: Getting Started : Agent-based Workflow with LangGraph

Lecture 3: Getting Started : Compile and Run the App (with Streamlit)

Lecture 4: Agentic RAG : Build a Multi-agent Workflow as Graph

Lecture 5: Define the Nodes

Lecture 6: Define the Edges

Lecture 7: Build the Workflow with Langraph

Lecture 8: Compile and Run the Workflow

Chapter 7: Enhanced RAG quality – Conventional vs. Structured RAG (unstructured.io, GPT-4)

Lecture 1: INTRO – Semi-structured RAG : to manage multiple data sources and content

Lecture 2: Extract elements from PDF : tables, images

Lecture 3: Describe images with GPT-4 Vision

Lecture 4: Process data sources into documents, index, retrieve and generate with LLM

Instructors

Sandra L. Sorel
Software Developer (Javascript | ReactJS | DApp | Web3 | AI)

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 7 votes

5 stars: 11 votes

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