Course Description

Master the art of Retrieval-Augmented Generation (RAG) systems with RAG Essentials. This hands-on course offers a comprehensive understanding of RAG components, workflows, and integration with LLMs to design powerful, intelligent systems. Whether you're an AI professional or a curious learner, this course equips you with the skills to build efficient retrieval systems and deploy RAG solutions for real-world applications.

Course curriculum

  • 1
    Introduction to RAG Systems
    • Course Introduction
    • Why RAG Systems
    • Course Handouts (updated on 2024-12-18)
    • What is a RAG System
    • Prompt Engineering vs. RAG vs. Fine-Tuning
    • RAG vs. Agents vs. Agentic RAG
    • Real-World RAG System Architectures
    • Quiz
    • Reading Resources
  • 2
    Building Retrieval Systems - Loading Data
    • Introduction to Retrieval
    • Document Loaders
    • Hands-On - Document Loaders - Part I
    • Hands-On - Document Loaders - Part II
    • Hands-On - Document Loaders - Part III
    • Hands-On - Document Loaders - Part IV
    • Quiz
    • Reading Resources
  • 3
    Building Retrieval Systems - Splitting and Chunking Data
    • Document Splitters and Chunkers
    • Hands-On - Document Splitters and Chunkers - Part I
    • Hands-On - Document Splitters and Chunkers - Part II
    • Quiz
    • Reading Resources
  • 4
    Building Retrieval Systems - Vector Databases and Retrievers
    • Embedding Models
    • Hands-On - Embedding Models
    • Vector Databases
    • Hands-On - Vector Databases
    • Retrievers
    • Hands-On - Retrievers - Part I
    • Hands-On Retrievers (Part II)
    • Quiz
    • Reading Resources
  • 5
    Project: Build a Document Retriever Search Engine
    • Project Introduction
    • Loading and Processing Data
    • Building the Vector Database Index
    • Building the Search Engine
    • Reading Resources
  • 6
    Building RAG Systems
    • RAG System Recap
    • Hands-On: Build a Simple RAG System
    • Hands-On: Build a Contextual Retrieval based RAG System
    • Hands-On: Building a RAG System with Sources
    • Hands-On: Building a RAG System with Citations
    • Reading Resources
  • 7
    Evaluating RAG Systems
    • Introduction to RAG Evaluation Metrics
    • Popular RAG Evaluation Frameworks
    • Hands-On: Deep Dive into RAG Evaluation Metrics - Setup RAG System
    • Hands-On_ Deep Dive into RAG Evaluation Metrics - Retriever Metrics - I
    • Hands-On: Deep Dive into RAG Evaluation Metrics - Retriever Metrics - II
    • Hands-On: Deep Dive into RAG Evaluation Metrics - Generator Metrics - I
    • Hands-On: Deep Dive into RAG Evaluation Metrics - Generator Metrics - II
    • Hands-On: End-to-End RAG System Evaluation Concepts
    • Hands-On: End-to-End RAG System Evaluation - Implementation
    • Quiz
    • Reading Resources
  • 8
    Projects: Building Advanced RAG Systems
    • Project: Multi-user Conversational RAG System - Concepts
    • Project: Multi-user Conversational RAG System - Implementation - I
    • Project: Multi-user Conversational RAG System - Implementation - II
    • Project: Multimodal RAG System - Concepts
    • Project: Multimodal RAG System - Implementation - I
    • Project: Multimodal RAG System - Implementation - II
    • Brief on Agentic RAG Systems
    • Project: Develop a RAG system for Question Answering
    • Reading Resources
    • Course Conclusion

Who Should Enroll

  • Professionals: Expand your expertise in RAG systems to innovate workflows and enhance decision-making.

  • Aspiring Students: Learn the essentials of RAG systems and gain practical experience to stand out in the tech landscape.

Key Takeaways

  • Understand the fundamentals of RAG and its integration with LLMs

  • Learn how to create and optimize retrieval systems to handle diverse data sources.

  • Develop hands-on expertise in building intelligent RAG systems tailored to practical use cases.

About the Instructor

Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya

Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include "Top 10 Data Scientists in India, 2020," "40 under 40 Data Scientists, 2021," "Google Developer Expert in Machine Learning, 2019," and "Top 50 AI Thought Leaders, Global AI Hub, 2022," alongside global accolades and a Google Champion Innovator title in Cloud AI/ML, 2022.
About the Instructor

FAQ

  • What is RAG?

    Retrieval-Augmented Generation (RAG) integrates retrieval systems with LLMs to generate context-aware and precise responses by pulling relevant information from external sources.

  • Does the course include hands-on projects?

    Yes, the course offers hands-on projects, including building retrieval systems and RAG solutions, to ensure practical application of the concepts.

  • Will I receive a certificate upon completion?

    Yes, a certificate of completion will be provided after successfully completing the course and assessments.