Master RAG: Ultimate Retrieval-Augmented Generation Course

Learn RAG for LLMs and Advanced Retrieval Techniques | LangChain and Embeddings | Multi-Agent RAG | RAG Pipelines

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

Created by: Ligency Team
Helping Data Scientists Succeed

What Will I 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

Target audience

• 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