Do I need prior AI or coding experience to take this course?

No advanced background is needed. The course starts with the foundations and builds step by step so that beginners can follow along. Some basic comfort with technology will help during the hands-on lessons.

Who is this course designed for?

It is designed for beginners, developers, data professionals, product managers, students, and anyone who wants to build AI applications that answer from their own data.

What is Retrieval-Augmented Generation (RAG)?

RAG is an approach in which an AI system retrieves relevant information from a knowledge source before generating an answer. This grounds the response in real data. Lesson 01 covers the foundations.

Why is RAG important?

Language models on their own can give outdated or unsupported answers. RAG lets them draw on current, relevant information, which makes responses more accurate and trustworthy.

What is a RAG pipeline?

A RAG pipeline is the sequence of steps that turns a user's question into a grounded answer. Lesson 02 explains how the pipeline works and how its parts fit together.

What will I learn in the hands-on demo?

Lesson 03 shows the RAG pipeline in action through a practical walkthrough, so you can see the concepts working together in a real example.

Why does retrieval quality matter?

A RAG system can only answer well from what it retrieves. If the retrieved information is off-target, the answer suffers. Lesson 04 covers how to improve retrieval quality.

How does prompt engineering apply to RAG?

The prompt tells the model how to use the retrieved information. Well-written prompts lead to clearer, more reliable answers. Lesson 05 covers prompt engineering for RAG.

What will I build in the course?

In Lesson 06, you build a RAG assistant, applying the foundations, pipeline, retrieval improvements, and prompting techniques from the earlier lessons.

How is RAG different from simply using an AI chatbot?

A standard chatbot answers from what the model already knows. A RAG assistant first retrieves relevant information from a knowledge source, so its answers are grounded in that data.

Can RAG help reduce incorrect or made-up answers?

It can help. Grounding responses in retrieved information gives the model something reliable to work from, though good retrieval and prompting are still needed for the best results.

Do I need to be a programmer to take this course?

Not necessarily. The concepts are explained in accessible language, and the demo shows each step. Some technical comfort will help you follow the building lessons more easily.

Is this course useful for non-developers?

Yes. Product managers, analysts, and business professionals can use it to understand how RAG works and how to plan AI solutions built on their own data.

RAG Course for Beginners

Free RAG Course - Learn Retrieval-Augmented Generation

4.55950 Learners EnrolledBeginner Level

Learn how to build AI applications that answer from your own data with this free course. From RAG foundations and the RAG pipeline to a hands-on demo and building a complete RAG assistant, this course gives you the practical skills to make AI responses more accurate and grounded. Earn a free certificate when you finish.

RAG Skills you will learn

RAG Fundamentals
RAG Pipeline
Retrieval Techniques
Retrieval Quality
Prompt Engineering
RAG Assistant

Who should learn this free RAG course?

Beginners
Software Developers
Data Professionals
Data Analysts
Product Managers
Students
Fresh Graduates
Prompt Engineers
IT Professionals

What you will learn in this free RAG course?

RAG Course for Beginners
- Introduction
  01:34
  - Introduction
    01:34
- Lesson 1: RAG Architecture
  32:34
  - RAG Architecture
    32:34
- Lesson 2: RAG Architecture Coding Demonstration
  46:49
  - RAG Architecture Coding Demonstration
    46:49
- Lesson 3: Transformer Models in NLP
  33:13
  - Transformer Models in NLP
    33:13
- Lesson 4: Transformer Architecture with Self-attention
  53:42
  - Transformer Architecture with Self-attention
    53:42

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

Why you should learn RAG?

$503.40 Billion

Expected size of global Machine Learning market by 2030.

$161K+ (USA) | INR 10 LPA

Average Salary of an ML Engineer annually.

FAQs

Do I need prior AI or coding experience to take this course?
No advanced background is needed. The course starts with the foundations and builds step by step so that beginners can follow along. Some basic comfort with technology will help during the hands-on lessons.
Who is this course designed for?
It is designed for beginners, developers, data professionals, product managers, students, and anyone who wants to build AI applications that answer from their own data.
What is Retrieval-Augmented Generation (RAG)?
RAG is an approach in which an AI system retrieves relevant information from a knowledge source before generating an answer. This grounds the response in real data. Lesson 01 covers the foundations.
Why is RAG important?
Language models on their own can give outdated or unsupported answers. RAG lets them draw on current, relevant information, which makes responses more accurate and trustworthy.
What is a RAG pipeline?
A RAG pipeline is the sequence of steps that turns a user's question into a grounded answer. Lesson 02 explains how the pipeline works and how its parts fit together.
What will I learn in the hands-on demo?
Lesson 03 shows the RAG pipeline in action through a practical walkthrough, so you can see the concepts working together in a real example.
Why does retrieval quality matter?
A RAG system can only answer well from what it retrieves. If the retrieved information is off-target, the answer suffers. Lesson 04 covers how to improve retrieval quality.
How does prompt engineering apply to RAG?
The prompt tells the model how to use the retrieved information. Well-written prompts lead to clearer, more reliable answers. Lesson 05 covers prompt engineering for RAG.
What will I build in the course?
In Lesson 06, you build a RAG assistant, applying the foundations, pipeline, retrieval improvements, and prompting techniques from the earlier lessons.
How is RAG different from simply using an AI chatbot?
A standard chatbot answers from what the model already knows. A RAG assistant first retrieves relevant information from a knowledge source, so its answers are grounded in that data.
Can RAG help reduce incorrect or made-up answers?
It can help. Grounding responses in retrieved information gives the model something reliable to work from, though good retrieval and prompting are still needed for the best results.
Do I need to be a programmer to take this course?
Not necessarily. The concepts are explained in accessible language, and the demo shows each step. Some technical comfort will help you follow the building lessons more easily.
Is this course useful for non-developers?
Yes. Product managers, analysts, and business professionals can use it to understand how RAG works and how to plan AI solutions built on their own data.