Master RAG: Ultimate Retrieval-Augmented Generation Course | Udemy [Update 07/2024]
English | Size: 2.2 GB
Genre: eLearning[/center]
Learn RAG for LLMs and Advanced Retrieval Techniques | LangChain and Embeddings | Multi-Agent RAG | RAG Pipelines
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
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
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