AI Engineering Bootcamp: RAG (Retrieval Augmented Generation) for LLMs

22h 1m 6s
English
Paid

Course description

This course will teach you how to create more intelligent AI applications using one of the most important techniques in modern artificial intelligence - Retrieval Augmented Generation (RAG). You will learn how to combine Large Language Models (LLMs) with RAG to develop advanced projects such as chatbots, financial analysis systems, and more.
Read more about the course

Why is RAG so important?

The limitations of many AI systems are related to their reliance on outdated data from their training samples. RAG addresses this issue by providing access to up-to-date information from external sources, including databases and documents. This makes AI more accurate and useful in real-world scenarios.

Example:

A chatbot in an online store can instantly check the current inventory based on real-time data, instead of relying on static training data, and give you an accurate answer about product availability and delivery times.

What you will learn:

  1. Basics of retrieval systems:
    • How to prepare textual data for search
    • Various search models (Boolean, vector, probabilistic)
    • Indexing, queries, and data ranking
  2. Basics of generative models:
    • Transformer architecture and attention mechanisms
    • Data preparation and text model training
  3. Introduction to Retrieval-Augmented Generation:
    • Combination of search and generation
    • Key principles and application of RAG in real tasks
  4. Working with OpenAI API:
    • API setup and effective use of prompts
    • Configuration parameters and their impact on model behavior
  5. Implementation of RAG with OpenAI:
    • Building fully functional RAG systems
    • Integrating search and generation to solve complex tasks
  6. Working with unstructured data:
    • Processing data from various formats: PDF, Word, PowerPoint, Excel, and images
    • Extracting valuable information from texts and multimedia
  7. Multimodal RAG systems:
    • Using textual and visual data to expand system capabilities
    • Integrating different types of data into a single response
  8. Agent systems with RAG:
    • Building AI agents capable of interacting with users and performing tasks
    • Managing agent states and dynamically executing tasks

Why do you need this course?

You will gain practical skills that will allow you to apply RAG in real projects and build scalable AI applications capable of processing complex queries and dynamically providing up-to-date answers.

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#1: Course Outline

All Course Lessons (195)

#Lesson TitleDurationAccess
1
Course Outline Demo
08:12
2
Meet Rubber Ducky! Your AI Course Assistant using RAG
06:05
3
Overview: Fundamentals of Retrieval Systems
04:05
4
Overview of Information Retrieval
05:38
5
What is Tokenization?
07:21
6
OpenAI Tokenizer
06:13
7
Libraries and Data Handling for RAG
06:36
8
Tokenization Techniques
07:00
9
Preprocessing Steps
09:36
10
Types of Retrieval Systems
07:07
11
Vector Space Model (TF-IDF)
09:45
12
Implementing TF-IDF - Part 1
07:52
13
Implementing TF-IDF - Part 2
04:16
14
TF-IDF Function and Output Analysis
04:58
15
Boolean Retrieval Model
06:04
16
Preprocessing Steps - Part 2
06:56
17
Setting a Directory
06:05
18
Boolean Retrieval Implementation
08:43
19
Probabilistic Retrieval Model
07:15
20
Probabilistic Retrieval Model Setup
03:33
21
Probabilistic Retrieval Model
05:38
22
How Google Search Works
11:30
23
Key Concepts: Indexing, Querying, and Ranking
07:23
24
What Did You Learn in This Section?
02:32
25
ReAct Prompt Engineering
11:53
26
Chain of Thought Prompt Engineering
14:25
27
Overview: Generative AI Fundamentals
01:47
28
Introduction to Text Generation
04:10
29
Understanding Transformers
12:48
30
Rock-Paper-Scissors, Dices and Strawberries
08:25
31
Getting a Hugging Face Key
04:20
32
Langchain and Hugging Face Setup
13:09
33
Basic Text Generation
07:35
34
Attention Mechanisms
06:15
35
Understanding Generation Model Parameters
06:23
36
System Message and Parameters
15:00
37
Text Generation with System Message
06:15
38
Text Generation with Parameters
10:49
39
OpenAI Playground - top P
04:52
40
What Did You Learn in This Section?
01:40
41
LLMs, Few-shot, Scaling and Factuality
14:26
42
Overview: RAG Fundamentals
02:39
43
Introduction to RAG Architecture
05:18
44
Hugging Face Setup
06:30
45
Tokenization and Embeddings for RAG
09:03
46
FAISS Index: Efficient Similarity Search
04:16
47
Building a Retrieval System
04:31
48
Developing a Generative Model
07:42
49
Implementing the RAG System
07:53
50
What Did You Learn in this Section?
03:10
51
LongRAG and LightRAG
16:41
52
Overview: Working with the OpenAI API
03:48
53
OpenAI API for Text
08:48
54
Setting Up OpenAI API Key
05:50
55
OpenAI API Setup
04:32
56
Generating Text with OpenAI API
07:03
57
OpenAI API Parameters
10:21
58
OpenAI API for Images
08:15
59
With Image URL
04:55
60
Converting Images to Base64
03:50
61
Assess My Python Course Thumbnail
04:49
62
What Did You Learn in this Section?
03:51
63
Project Briefing: Customer Acquisition
06:08
64
OpenAI Setup
05:40
65
AI Agent System Prompt
08:22
66
Processing Images for GenAI
05:25
67
Extract Data with GenAI
13:39
68
Improving GenAI Extraction
06:19
69
GenAI with all Images
06:56
70
PDF to Images
10:32
71
Wrapping Up the OpenAI GenAI Project
08:17
72
Overview: RAG with OpenAI GPT Models
04:35
73
Case Study Briefing: Cooking Books
04:58
74
Converting PDF to Images
09:16
75
Reading a Single Image with GPT
12:04
76
Enhancing AI with Prompt Engineering
09:11
77
Reading All Images in a Dataset
05:08
78
Filtering Non-relevant Information
06:04
79
Understanding Embeddings in NLP
06:51
80
Generating Embeddings
13:57
81
Building FAISS Index and Metadata Integration
06:28
82
Implementing a Robust Retrieval System
14:42
83
Combining Outputs for Enhanced Results
02:57
84
Constructing a Generative Model
11:43
85
Complete RAG System Implementation
06:42
86
How to Improve RAG Systems Effectively?
07:04
87
Overview: Working With Unstructured Data
03:37
88
Introduction to Langchain Library
07:27
89
Excel Data: Best Practices for Data Handling
06:42
90
Python - Initial Setup for Data Processing
05:48
91
Loading Data and Implementing Chunking Strategies
05:14
92
Developing a Retrieval System for Unstructured Data
06:11
93
Building a Generation System for Dynamic Content
09:13
94
Building Retrieval and Generation Functions
09:58
95
Working with Word Documents
04:55
96
Setting Up Word Documents for RAG
06:18
97
Implementing RAG for Word Documents
02:27
98
Working with PowerPoint Presentations
04:45
99
PowerPoint Setup for RAG
04:12
100
RAG Implementation for PowerPoint
03:10
101
Working with EPUB Files
04:59
102
EPUB Setup for RAG
04:48
103
RAG Implementation for EPUB Files
02:23
104
Working with PDF Files
04:22
105
PDF Setup for RAG
05:52
106
RAG Implementation for PDF Files
05:38
107
What Did You Learn in This Section?
03:57
108
Exercise: Imposter Syndrome
02:57
109
Overview: Multimodal RAG
03:39
110
Introduction to Multimodal RAG
05:59
111
Setup and Video Processing
05:24
112
Extracting Audio from Video
08:45
113
Compressing Audio Files
04:18
114
Transcribing Audio with OpenAI Whisper
10:08
115
Whisper Model
06:32
116
Extracting Frames from Video
05:50
117
Introduction to Contrastive Learning
05:15
118
Understanding the CLIP Model
05:23
119
Tokenizing Text for Multimodal Tasks
08:14
120
Chunking and Embedding Text
11:37
121
Embedding Images for Multimodal Analysis
08:37
122
Understanding Cosine Similarity in Multimodal Contexts
06:47
123
Applying Contrastive Learning and Cosine Similarity
10:27
124
Visualizing Text and Image Embeddings
11:12
125
Query Embedding Techniques
04:13
126
Calculating Cosine Similarity for Query and Text
11:48
127
GenAI Model Setup for Multimodal Tasks
04:56
128
Building a GenAI Model
07:12
129
What Did You Learn in This Section?
02:13
130
Project Briefing: Starbucks Financial Data
05:28
131
Transcribing Audio with OpenAI Whisper
11:23
132
Embedding Transcription with CLIP
07:36
133
Converting PDF to Images
05:58
134
Embedding Images for Multimodal Analysis
04:59
135
Retrieval System
17:14
136
Preparing Context
05:00
137
Generative System
12:47
138
RAG with OpenAI File Search
08:32
139
Vector Stores in OpenAI
05:53
140
Setting a Vector Store in the OpenAI API
05:47
141
Responses Endpoint with File Search
07:28
142
RAG with GPT-4.1-mini
07:00
143
RAG with System Developper / Messages
05:35
144
Overview: Agentic RAG
02:52
145
AI Agents
07:52
146
Agentic RAG
05:45
147
Setup and Data Loading
09:55
148
State Management and Memory in Agentic Systems
07:55
149
AgentState Class
04:30
150
Greeting the Customer
04:53
151
AI Agent that Checks the Question
10:48
152
AI Agent that Assesses the Validity of the question
07:23
153
Retrieving the Documents
05:47
154
Testing the App
07:14
155
Generate Answers
09:22
156
AI Agent that Improves the Answer
11:14
157
Asking User For More Questions
05:30
158
Agentic RAG Recap - Key Learnings and Next Steps
06:18
159
Game Plan for Knowledge Graphs with LightRAG
02:20
160
Knowledge Graphs
07:20
161
Knowledge Graphs vs Embeddings
08:50
162
LightRAG Setup
07:36
163
What is LightRAG?
04:41
164
Setting the Working Directory
02:29
165
Data Prep
04:49
166
Naive RAG
06:08
167
Implementing LightRAG
06:11
168
Knowledge Graph Visualization
08:21
169
Local Knowledge Graph Visualization
06:16
170
Game Plan for RAGAS
01:54
171
Assessing RAG with RAGAS
06:14
172
RAGAS Setup
03:51
173
Embedding and Facebook AI Similarity Search (FAISS)
09:52
174
Python - RAG
11:37
175
Synthetic Data
03:37
176
Generating Synthetic Data
04:59
177
Python - Answering Synthetic Dataset
06:29
178
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score
05:33
179
ROUGE
13:50
180
LLM-Based Assessment
06:08
181
Simple Criteria Score - Part 1
05:36
182
Simple Criteria Score - Part 2
05:40
183
Factual Correctness
05:17
184
Rubrics Score
04:53
185
Semantic Similarity
04:47
186
Factual Correctness
05:17
187
Context Precision
03:13
188
Semantic Similarity
04:58
189
Context Recall
03:12
190
Context Precision
05:58
191
Response Relevancy
04:37
192
Context Recall
04:56
193
Response Relevancy
06:23
194
Key Learnings and Outcomes: RAGAS
03:18
195
Thank You!
01:18

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