Introduction to RAG

In Collaboration With
CohereCohere

Course Outline

This course provides a comprehensive guide to building and implementing Retrieval-Augmented Generation (RAG) systems using semantic search and AI-powered tools. You will learn how to set up a structured environment for development, create and process embeddings, perform semantic search, and integrate retrieved information with language models to generate contextually relevant responses.

Learning Outcomes

  • Establish baseline understanding of RAG
  • Learn about the benefits of RAG
  • Create a RAG enabled chatbot

Who Is This Course For?

  • AI enthusiasts and beginners interested in learning the basics of Retrieval-Augmented Generation and semantic search
  • Data scientists and developers who want to enhance their projects with AI-powered search capabilities
  • Business professionals and entrepreneurs looking to implement intelligent retrieval systems within applications
  • Educators and tech learners aiming to understand AI retrieval systems from a practical, hands-on perspective

Pre-requisites

  • Basic understanding of Python: Familiarity with running Python code and basic coding syntax is helpful.
  • No prior experience with AI or Jupyter Notebook is required: The course includes step-by-step instructions suitable for beginners.
  • Interest in learning AI tools and concepts: A curiosity about AI and data retrieval will enhance your learning experience.
  • Cohere API key: You can use a free trial key
LevelBeginner
Your Instructor
AP
Arun Prasad
Duration3 hours
Showcasing
CohereCohere
Progress0/8 chapters complete
0%
Your certificateComplete course to get your certificate
Certificate of Completion
Course Name
Why Enroll?
If you want to build intelligent search and retrieval systems that move beyond basic keyword matching, this course will give you the skills to do just that. Retrieval-Augmented Generation (RAG) is a powerful technique that allows AI to understand and respond to queries based on meaning rather than simply matching words. By learning RAG, you can create smarter systems that retrieve and generate information more accurately.
Through hands-on modules, you will gain practical skills in setting up and configuring a Jupyter Notebook environment, essential for working with AI projects. You will also learn how to use embeddings and semantic search to add depth to your search results, allowing the system to respond based on context. By the end of the course, you will be able to implement RAG in real-world applications, creating meaningful user experiences through enhanced retrieval and generation capabilities.
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