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!

Who this course is for:

• 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