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

Name: AI Engineering Bootcamp: RAG (Retrieval Augmented Generation) for LLMs
Price: 9 USD
Availability: InStock

22h 1m 6s

English

Paid

July 17, 2025

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.

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

Join premium to watch

Go to premium

#	Title	Duration
1	Course Outline	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