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 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Language models on their own can give outdated or unsupported answers.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 happens 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.

What kinds of applications can I build with RAG?

RAG suits any assistant that answers from specific information, such as document question-answering, internal knowledge assistants, and support tools. The final lesson shows how to build one.

Do I need special tools or software?

You can follow the lessons as they are presented. To practice the demo and build your own assistant, you will need a computer with internet access.

Free RAG Course for Beginners

Beginner 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.

Skills you will learn

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

Who should learn

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

What you will learn

Free RAG Course for Beginners
- Introduction
  01:04
  - 0.01 Introduction
    01:04
- Lesson 01: RAG Fundamentals
  04:19
  - 1.01 RAG Fundamentals
    04:19
- Lesson 02: RAG Pipeline
  05:16
  - 2.01 Rag Pipeline
    05:16
- Lesson 03: Hands: On Demo
  16:29
  - 3.01 Hands On Demo
    16:29
- Lesson 04: Improving Retrieval Quality
  11:45
  - 4.01 Improving Retrieval Quality
    11:45
- Lesson 05: Prompt Engineering for RAG
  05:36
  - 5.01 Prompt Engineering For RAG
    05:36
- Lesson 06: Building a RAG Assistant
  04:00
  - 6.01 Building a RAG Assistant
    04:00

Get a Completion Certificate

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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 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 happens 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.
What kinds of applications can I build with RAG?
RAG suits any assistant that answers from specific information, such as document question-answering, internal knowledge assistants, and support tools. The final lesson shows how to build one.
Do I need special tools or software?
You can follow the lessons as they are presented. To practice the demo and build your own assistant, you will need a computer with internet access.
How is this course different from a general prompt engineering course?
A general prompt engineering course focuses on writing prompts. This course covers the full RAG system, including the pipeline and retrieval, and applies prompt engineering specifically to retrieved information.
Is there a certificate included?
Yes. You receive a free certificate upon completion, and you can add it to your LinkedIn profile or resume right away.
How long does the course take, and is it self-paced?
The course is fully self-paced with no fixed deadlines. It works best when taken in order, since each lesson builds on the one before.
What should I do after finishing this course?
Build a RAG assistant on your own set of documents and keep refining its retrieval and prompts. Improving a real project is what turns the course into lasting skill.