CourseFlix

Introduction to RAG

2h 23m 5s
English
Paid

Unlock the potential of Retrieval-Augmented Generation (RAG) as you delve into this comprehensive course designed to equip you with the skills to create efficient and reliable applications. Embrace the journey to mastering RAG systems, exploring advanced concepts like Agentic RAG systems, and gain the methodologies to develop diverse applications across many fields.

Course Requirements

  • If you are not familiar with advanced methods of prompt writing for LLM, it is recommended to first complete the courses "Introduction to Prompt Engineering" and "Advanced Prompt Engineering".
  • The primary tool for the course is Flowise AI, a popular no-code platform for building complex RAG and agent workflows. No programming is required.
  • Detailed instructions for installing and accessing Flowise AI are provided in the course materials.

Course Topics

Throughout this course, students will engage with Flowise AI, simplifying the development of complex agent workflows. Here's an overview of the main topics covered:

1. Introduction to RAG

  • Basic principles of Retrieval-Augmented Generation
  • Advantages over traditional generation methods
  • Key application areas

2. RAG Architecture

  • Technical structure of RAG systems
  • Data chunking methods
  • Embedding models
  • Vector databases and semantic search
  • Interaction between retriever and generator components

3. Creating Simple RAG Systems

  • Practical creation of an initial RAG system
  • Developing a personalized tutor using RAG

4. Developing a RAG Chat Assistant

  • Application of RAG in chatbots, catering to popular business scenarios
  • Creating an online chat assistant for customer support
  • Setting up document storage and integration with RAG
  • Enhancing search quality with techniques like query expansion

5. Advanced RAG

  • Implementing enhanced prompting techniques including:
    • Tool calling
    • Chain-of-Thought prompting (CoT)
    • Prompt chaining
  • Developing a complex RAG application integrating LLM concepts

6. Agentic RAG Systems

  • Integrating AI agents into RAG systems with a modern approach
  • Utilizing function calling to expand RAG capabilities
  • Developing an Agent RAG application for interactions with external tools:
    • Calculator
    • Logical reasoning tool
    • Chain of LLM calls

7. Deployment of RAG Applications

  • Creating an online application with sharing features
  • Applying best practices to enhance RAG performance

Who Will Benefit from This Course

This course is ideal for professionals in artificial intelligence, data analytics, business process automation, customer support, research, and programming, as well as anyone interested in learning about Retrieval-Augmented Generation.

Companies Whose Employees Have Taken Our Courses

Our training participants include employees from prestigious companies such as Google, OpenAI, Microsoft, Meta, JPMorgan Chase & Co, Amazon, Salesforce, Airbnb, Apple, Intel, Khan Academy, Oracle, LinkedIn, Walmart, Fidelity Investments, and many others.

Upon completing the course, students will be proficient in developing and implementing RAG applications that effectively combine information retrieval and answer generation to tackle various business challenges.

About the Author: DAIR.AI (Elvis Saravia)

DAIR.AI (Elvis Saravia) thumbnail

DAIR.AI (Democratizing Artificial Intelligence Research) is the educational arm founded by Elvis Saravia, a former Meta AI researcher and the maintainer of one of the most-starred prompt-engineering reference repositories on GitHub. The brand has become one of the more authoritative independent sources on the practical engineering side of LLM applications.

The CourseFlix listing carries five DAIR.AI courses spanning the applied AI track: Introduction to Prompt Engineering, Advanced Prompt Engineering, Introduction to RAG, Introduction to AI Agents, and Cursor — Coding with AI.

Material is paid and aimed at engineers picking up applied LLM and AI-coding work as deliberate professional skills. For broader content, see CourseFlix's Prompt Engineering, RAG, AI Agents, and AI-Assisted Coding category pages.

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#1: Course Introduction
All Course Lessons (27)
#Lesson TitleDurationAccess
1
Course Introduction Demo
04:15
2
What is RAG?
01:39
3
RAG Components
01:40
4
Why do we need RAG?
03:41
5
RAG Common Use Cases
02:26
6
Introduction to Flowise AI
04:10
7
Create a Basic Chatflow
05:47
8
Introduction to RAG Architecture
02:41
9
Chunking
03:04
10
Embedding Model
01:36
11
What is Semantic Search?
04:00
12
Retriever
02:33
13
Generator & RAG Enhancements
05:14
14
Build a RAG System from Scratch
13:50
15
RAG Chat Assistant
01:41
16
Build a Document Store
10:28
17
Build a RAG Chat Assistant
08:47
18
Query Expansion
08:46
19
Advanced RAG System
06:23
20
Chain-of-Thought Prompting
05:17
21
RAG + Tool Calling
07:59
22
What is Agentic RAG?
02:32
23
What is Function Calling?
02:14
24
Build an Agentic RAG System
14:11
25
Creating an Online Document Store
03:25
26
Online RAG Application
06:57
27
Conclusions
07:49
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Course content

27 lessons · 2h 23m 5s
Show all 27 lessons
  1. 1 Course Introduction 04:15
  2. 2 What is RAG? 01:39
  3. 3 RAG Components 01:40
  4. 4 Why do we need RAG? 03:41
  5. 5 RAG Common Use Cases 02:26
  6. 6 Introduction to Flowise AI 04:10
  7. 7 Create a Basic Chatflow 05:47
  8. 8 Introduction to RAG Architecture 02:41
  9. 9 Chunking 03:04
  10. 10 Embedding Model 01:36
  11. 11 What is Semantic Search? 04:00
  12. 12 Retriever 02:33
  13. 13 Generator & RAG Enhancements 05:14
  14. 14 Build a RAG System from Scratch 13:50
  15. 15 RAG Chat Assistant 01:41
  16. 16 Build a Document Store 10:28
  17. 17 Build a RAG Chat Assistant 08:47
  18. 18 Query Expansion 08:46
  19. 19 Advanced RAG System 06:23
  20. 20 Chain-of-Thought Prompting 05:17
  21. 21 RAG + Tool Calling 07:59
  22. 22 What is Agentic RAG? 02:32
  23. 23 What is Function Calling? 02:14
  24. 24 Build an Agentic RAG System 14:11
  25. 25 Creating an Online Document Store 03:25
  26. 26 Online RAG Application 06:57
  27. 27 Conclusions 07:49

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Frequently asked questions

What is Introduction to RAG about?
Unlock the potential of Retrieval-Augmented Generation (RAG) as you delve into this comprehensive course designed to equip you with the skills to create efficient and reliable applications. Embrace the journey to mastering RAG systems…
Who teaches Introduction to RAG?
Introduction to RAG is taught by DAIR.AI (Elvis Saravia). You can find more courses by this instructor on the corresponding source page.
How long is Introduction to RAG?
Introduction to RAG contains 27 lessons with a total runtime of 2 hours 23 minutes. All lessons are available to watch online at your own pace.
Is Introduction to RAG free to watch?
Introduction to RAG is part of CourseFlix's premium catalog. A CourseFlix subscription unlocks the full video player; the course description, table of contents, and preview information are available to everyone.
Where can I watch Introduction to RAG online?
Introduction to RAG is available to watch online on CourseFlix at https://courseflix.net/course/introduction-to-rag. The page hosts every lesson with the integrated video player; no download is required.