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!
