What is RAG?

Free RAG Course for Beginners: Learn Retrieval-Augmented Generation, LLMs and AI Knowledge Bases

Retrieval-Augmented Generation, or RAG, is one of the most important ideas behind practical AI systems.

Instead of asking a large language model to answer only from memory, RAG connects the model to external knowledge sources such as documents, databases, websites, PDFs, internal files or company knowledge bases. The AI can retrieve relevant information first, then use its language skills to generate a more accurate, useful and context-aware answer.

What is RAG? is a free beginner-friendly course from NORAI Academy. It explains Retrieval-Augmented Generation in plain English and shows why RAG matters for modern AI tools, LLM applications, private chatbots, research systems, customer support, knowledge engines and business automation.

You do not need coding experience or a technical background to begin.

In this course, you’ll learn how RAG works, why it helps reduce AI hallucinations, how it gives AI access to current and domain-specific information, and why it can be more practical than retraining or fine-tuning a model for every new task.

You’ll explore the main parts of a RAG system, including the knowledge base, document chunking, retrieval, integration layer and generator. You’ll also learn where RAG is useful in the real world: specialised chatbots, internal company assistants, research tools, content generation, market analysis, product development, recommendation services and secure knowledge-based AI systems.

This course is designed as a simple starting point for understanding how LLMs can work with external information.

Who is this for?

This course is for anyone who wants to understand how modern AI systems can use trusted information instead of guessing.

It is suitable for:

– beginners who want to understand RAG without technical jargon
– students learning about large language models and AI architecture
– professionals curious about AI knowledge bases and private chatbots
– entrepreneurs and small business owners exploring practical AI tools
– consultants, marketers and creators who want more reliable AI workflows
– developers and technical learners who want a conceptual introduction before building RAG systems
– anyone interested in reducing hallucinations and improving AI accuracy

By the end of the course, you will understand what Retrieval-Augmented Generation is, how RAG systems work, why retrieval matters, how documents are split into chunks, what a retriever does, how the generator uses retrieved context, and why RAG is useful for building more reliable, up-to-date and domain-aware AI applications.

Each section concludes with a short quiz to reinforce learning. Quizzes are scored, and a minimum of 80% is required to pass.

Inspirational Source:
IBM / Ivan Belcic

FAQ

What is RAG in AI? 
RAG stands for Retrieval-Augmented Generation. It is an AI approach that connects a language model to external information sources so it can retrieve relevant context before generating an answer.

Why is RAG useful? 
RAG helps AI systems give more accurate, current and domain-specific responses. It can reduce hallucinations by grounding answers in documents, databases or other trusted sources.

Do I need coding skills for this RAG course? 
No. This course is beginner-friendly and explains the concepts in plain English before going into technical implementation.

What is a RAG knowledge base? 
A RAG knowledge base is the collection of documents, data or information sources the AI system can search when answering a question.

What is chunking in RAG? 
Chunking means splitting documents into smaller sections so the AI system can retrieve the most relevant pieces of information instead of searching one large document at once.

Who is this RAG course for? 
This course is for beginners, students, professionals, entrepreneurs, consultants and technical learners who want to understand how RAG makes AI systems more useful, accurate and context-aware.