What is RAG?

About Course

Retrieval-Augmented Generation (RAG) is an AI architecture designed to optimise the performance of large language models (LLMs) by connecting them to external knowledge sources. By supplementing model outputs with real-time, relevant data, RAG enables LLMs to deliver responses that are more accurate, up-to-date, and contextually appropriate.

In this course, you will learn the fundamentals of RAG and gain a clear understanding of how retrieval-augmented generation works in practice.

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

Course Content

What Are the Benefits of RAG?
RAG empowers organizations to avoid high retraining costs when adapting generative AI models to domain-specific use cases. Enterprises can use RAG to complete gaps in a machine learning model’s knowledge base so it can provide better answers.

The Primary Benefits of RAG
Cost-effective AI Implementation and Scalable Deployment
Access to Current and Domain-Specific Data
Lower Risk of AI Hallucinations
Increased User Trust
Expanded Use Cases
Enhanced Developer Control and Model Maintenance
Greater Data Security
The Benefits of RAG

RAG Use Cases
RAG systems essentially enable users to query databases with conversational language. The data-powered question-answering abilities of RAG systems have been applied across a range of use cases.

How Does RAG Work?
RAG works by combining information retrieval models with generative AI models to produce more authoritative content. RAG systems query a knowledge base and add more context to a user prompt before generating a response.

Components of a RAG System
RAG systems contain four primary components.

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About Course

What Will You Learn?

Course Content

What Are the Benefits of RAG?
RAG empowers organizations to avoid high retraining costs when adapting generative AI models to domain-specific use cases. Enterprises can use RAG to complete gaps in a machine learning model’s knowledge base so it can provide better answers.

The Primary Benefits of RAG

Cost-effective AI Implementation and Scalable Deployment

Access to Current and Domain-Specific Data

Lower Risk of AI Hallucinations

Increased User Trust

Expanded Use Cases

Enhanced Developer Control and Model Maintenance

Greater Data Security

The Benefits of RAG

RAG Use Cases
RAG systems essentially enable users to query databases with conversational language. The data-powered question-answering abilities of RAG systems have been applied across a range of use cases.

RAG Systems in Action

Specialised Chatbots and Virtual Assistants

Research

Content Generation

Market Analysis and Product Development

Knowledge Engines

Recommendation Services

RAG Use Cases

How Does RAG Work?
RAG works by combining information retrieval models with generative AI models to produce more authoritative content. RAG systems query a knowledge base and add more context to a user prompt before generating a response.

RAG Works by Combining Information Retrieval with Generative AI

RAG Systems Follow a Five-Stage Process

How RAG Systems Work

Components of a RAG System
RAG systems contain four primary components.

RAG Systems Contain Four Primary Components

The Knowledge Base

Chunking

The Retriever

The Integration Layer

The Generator

RAG System Components

Earn a FREE certificate on completion!

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About Course

What Will You Learn?

Course Content

What Are the Benefits of RAG? RAG empowers organizations to avoid high retraining costs when adapting generative AI models to domain-specific use cases. Enterprises can use RAG to complete gaps in a machine learning model’s knowledge base so it can provide better answers.

The Primary Benefits of RAG

Cost-effective AI Implementation and Scalable Deployment

Access to Current and Domain-Specific Data

Lower Risk of AI Hallucinations

Increased User Trust

Expanded Use Cases

Enhanced Developer Control and Model Maintenance

Greater Data Security

The Benefits of RAG

RAG Use Cases RAG systems essentially enable users to query databases with conversational language. The data-powered question-answering abilities of RAG systems have been applied across a range of use cases.

RAG Systems in Action

Specialised Chatbots and Virtual Assistants

Research

Content Generation

Market Analysis and Product Development

Knowledge Engines

Recommendation Services

RAG Use Cases

How Does RAG Work? RAG works by combining information retrieval models with generative AI models to produce more authoritative content. RAG systems query a knowledge base and add more context to a user prompt before generating a response.

RAG Works by Combining Information Retrieval with Generative AI

RAG Systems Follow a Five-Stage Process

How RAG Systems Work

Components of a RAG System RAG systems contain four primary components.

RAG Systems Contain Four Primary Components

The Knowledge Base

Chunking

The Retriever

The Integration Layer

The Generator

RAG System Components

Earn a FREE certificate on completion!

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What Are the Benefits of RAG?
RAG empowers organizations to avoid high retraining costs when adapting generative AI models to domain-specific use cases. Enterprises can use RAG to complete gaps in a machine learning model’s knowledge base so it can provide better answers.

RAG Use Cases
RAG systems essentially enable users to query databases with conversational language. The data-powered question-answering abilities of RAG systems have been applied across a range of use cases.

How Does RAG Work?
RAG works by combining information retrieval models with generative AI models to produce more authoritative content. RAG systems query a knowledge base and add more context to a user prompt before generating a response.

Components of a RAG System
RAG systems contain four primary components.