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

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

18h 17m 22s

English

Paid

March 9, 2026

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.

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The limitations of many AI systems are related to their reliance on outdated data from their training samples.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:

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

About the Author: zerotomastery.io

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Watch Online 172 lessons

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#1: Course Outline

All Course Lessons (172)

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

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