Ultimate RAG Course

Developing Advanced Applications with Large Language Models (LLMs) and High-Level Frameworks

What you'll 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

Requirements

• Basics web development and programming skills (1-2 xp)
• Python programming Language (1-2 xp)
• Basic command line operations
• Latest version of Python (3.7+)
• A Code Editor (recommanded : Visual Studio Code)
• One first experience with building LLM-driven applications

Description

Welcome to "Ultimate RAG: From Basics to Advanced Techniques"!

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.

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!