Master RAG: Ultimate Retrieval-Augmented Generation Course
Master RAG: Ultimate Retrieval-Augmented Generation Course
Learn RAG for LLMs and Advanced Retrieval Techniques | LangChain and Embeddings | Multi-Agent RAG | RAG Pipelines
Product Brand: Udemy
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Udemy Coupon Code for 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, Ligency Team | 5 hours on-demand video course
Ultimate Retrieval-Augmented Generation Course Overview
Master RAG: Ultimate Retrieval-Augmented Generation Course
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.
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.
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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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Taught by Sandra L. Sorel
