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Free RAG Course with Certificate
Introduction to RAG
Enroll in our Introduction to Free RAG course, perfect for beginners. You'll learn the basics, key concepts, terminology, and the Retrieval augmented generation workflow. Start today and earn a certificate upon course completion.
Instructor:
Sunakshi MamgainModules updated 07/2025
Introduction to RAG
656 learners enrolled so far
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Skills you will gain
Introduction to RAG
Basics Concepts and Terminology - RAG
Workflow of RAG
Key Highlights

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About this course
Introduction to RAG is uniquely designed to give beginners a comprehensive understanding of RAG. Students can expect to learn the foundational concepts and terminology, gain insight into the RAG workflow, and explore its real-world applications. What sets this course apart is its focus on practical learning—addressing challenges like hallucinations in Retrieval augmented generation and providing hands-on experience with LangChain for implementation. By the end, you’ll clearly understand RAG and how to apply it effectively.
Course outline
Introduction to RAG
In this module you will learn Large Language Models (LLMs) fundamentals and how Retrieval-Augmented Generation (RAG) integrates external knowledge for more accurate responses.
Basics Concepts and Terminology - RAG
This module covers the basics of RAG focusing on key concepts, retrieval and generation processes, and how they enhance large language models with external knowledge for more accurate responses.
Workflow of RAG
This module explains the RAG workflow, detailing the retrieval of external information and generation of accurate responses with large language models. Learn how RAG enhances AI capabilities by combining these processes.
Applications of RAG
This module explores RAG applications in fields like customer support, content creation, and research. Discover real-world examples and case studies demonstrating RAG's practical benefits and versatility.
Hallucinations in RAG
This module addresses hallucinations in RAG, explaining their causes and impacts. Learn strategies to mitigate hallucinations and improve RAG model accuracy and reliability.
Implementing RAG using LangChain
This module provides a step-by-step guide to implementing RAG with LangChain, starting with library imports and progressing through practical exercises for enhanced AI responses.
Get access to the complete curriculum once you enroll in the course
Stand out with an industry-recognized certificate

10,000+ certificates claimed, get yours today!

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Globally recognised

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Introduction to RAG
3.0 Hours
Beginner
656 learners enrolled so far

Get free course content

Master in-demand skills & tools

Test your skills with quizzes
Our course instructor

Sunakshi Mamgain
Data Science and AI Mentor
ChatGPT and Generative AI Expert


Frequently Asked Questions
Will I receive a certificate upon completing this free course?
Is this course free?
What topics are covered in this course?
You'll cover topics such as the fundamentals of Large Language Models (LLMs), integrating external knowledge in RAG, key concepts and terminology, the RAG workflow, applications, handling hallucinations, and implementing RAG using LangChain.
What is the "Introduction to RAG" course about?
This course introduces beginners to the fundamentals of Retrieval-Augmented Generation (RAG). You'll learn key concepts, the RAG workflow, its applications in various fields, and practical implementation techniques.
Who is this course intended for?
The course is ideal for beginners or anyone interested in learning about RAG and its applications in AI. No prior knowledge of RAG is required, making it accessible to learners from various backgrounds.
How long does it take to complete the course?
The course duration is of 1:30:00 hours. It can be completed at your own pace. Most learners finish the course within a few hours, but you can take as much time as you need to understand the material fully.
Is the course free of charge?
Yes, this course is completely free. There are no hidden fees, and once you enroll, you’ll have full access to all the learning materials.
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