Autoplay
Autocomplete
Previous Lesson
Complete and Continue
AI Engineering Bootcamp: RAG (Retrieval Augmented Generation) for LLMs
Section 1: Introduction to Retrieval Augmented Generation (RAG) Systems
Course Outline (8:11)
Exercise: Meet Your Classmates and Instructor
Meet Rubber Ducky! Your AI Course Assistant using RAG (6:04)
Link to Your AI Course Assistant
ZTM Plugin + Understanding Your Video Player
[ACTION] Download the Course Resources
Set Your Learning Streak Goal
Section 2: Fundamentals of Retrieval Systems
Overview: Fundamentals of Retrieval Systems (4:04)
Overview of Information Retrieval (5:37)
What is Tokenization? (7:20)
OpenAI Tokenizer (6:12)
Libraries and Data Handling for RAG (3:15)
Tokenization Techniques (4:24)
Preprocessing Steps (8:57)
Types of Retrieval Systems (7:06)
Vector Space Model (TF-IDF) (9:44)
Implementing TF-IDF (6:07)
TF-IDF Function and Output Analysis (7:56)
Boolean Retrieval Model (3:57)
Boolean Retrieval Implementation (16:56)
Probabilistic Retrieval Model - Part 1 (7:14)
Probabilistic Retrieval Model - Part 2 (7:28)
How Google Search Works (11:29)
Key Concepts: Indexing, Querying, and Ranking (7:22)
What Did You Learn in This Section? (2:31)
Let's Have Some Fun (+ More Resources)
Section 3: The Science of Prompt Engineering
ReAct Prompt Engineering (11:52)
Chain of Thought Prompt Engineering (14:24)
Section 4: Generative AI Fundamentals
Overview: Generative AI Fundamentals (1:46)
Introduction to Text Generation (4:09)
Understanding Transformers (12:47)
Rock-Paper-Scissors, Dices and Strawberries (8:24)
Text Generation with GPT2 (12:51)
Tokenization for Text Generation (6:00)
Padding the Data for Consistency (5:05)
Attention Mechanisms (6:14)
Creating a Dataset Class (7:35)
Fine-Tuning the GPT-2 Model (8:32)
Generating Text with GPT-2 (4:13)
What Did You Learn in This Section? (1:39)
Unlimited Updates
Section 5: The Science of LLMs
LLMs, Few-shot, Scaling and Factuality (14:25)
Section 6: Retrieval-Augmented Generation (RAG) Fundamentals
Overview: RAG Fundamentals (2:38)
Introduction to RAG Architecture (5:17)
Tokenization and Embeddings for RAG (13:15)
FAISS Index: Efficient Similarity Search (4:15)
Building a Retrieval System (7:44)
Developing a Generative Model (11:00)
Implementing the RAG System (7:00)
Defining a Relevant Context Distance (11:39)
Understanding Generation Model Parameters (6:22)
Configuring RAG with Parameters (5:06)
What Did You Learn in this Section? (3:09)
Implement a New Life System
Section 7: The Science of RAG
LongRAG and LightRAG (16:40)
Section 8: Working with the OpenAI API
Overview: Working with the OpenAI API (3:47)
OpenAI API for Text (8:47)
Setting Up OpenAI API Key (5:49)
System Message and Parameters (14:59)
OpenAI API Setup (4:31)
Generating Text with OpenAI API (7:02)
OpenAI API Parameters (10:20)
OpenAI API for Images (8:14)
With Image URL (4:54)
Converting Images to Base64 (3:49)
Assess My Python Course Thumbnail (4:48)
What Did You Learn in this Section? (3:50)
Section 9: Project - Customer Acquisition
Project Briefing: Customer Acquisition (6:07)
OpenAI Setup (5:39)
AI Agent System Prompt (8:21)
Processing Images for GenAI (5:24)
Extract Data with GenAI (13:38)
Improving GenAI Extraction (6:18)
GenAI with all Images (6:55)
PDF to Images (10:31)
Wrapping Up the OpenAI GenAI Project (8:16)
Section 10: RAG with OpenAI GPT Models
Overview: RAG with OpenAI GPT Models (4:34)
Case Study Briefing: Cooking Books (4:57)
Converting PDF to Images (9:15)
Reading a Single Image with GPT (12:03)
Enhancing AI with Prompt Engineering (9:10)
Reading All Images in a Dataset (5:07)
Filtering Non-relevant Information (6:03)
Understanding Embeddings in NLP (6:50)
Generating Embeddings (13:56)
Building FAISS Index and Metadata Integration (6:27)
Implementing a Robust Retrieval System (14:41)
Combining Outputs for Enhanced Results (2:56)
Constructing a Generative Model (11:42)
Complete RAG System Implementation (6:41)
How to Improve RAG Systems Effectively? (7:03)
Course Check-In
Section 11: Working With Unstructured Data
Overview: Working With Unstructured Data (3:36)
Introduction to Langchain Library (7:26)
Excel Data: Best Practices for Data Handling (6:41)
Python - Initial Setup for Data Processing (5:47)
Loading Data and Implementing Chunking Strategies (5:13)
Developing a Retrieval System for Unstructured Data (6:10)
Building a Generation System for Dynamic Content (9:12)
Building Retrieval and Generation Functions (9:57)
Working with Word Documents (4:54)
Setting Up Word Documents for RAG (6:17)
Implementing RAG for Word Documents (2:26)
Working with PowerPoint Presentations (4:44)
PowerPoint Setup for RAG (4:11)
RAG Implementation for PowerPoint (3:09)
Working with EPUB Files (4:58)
EPUB Setup for RAG (4:47)
RAG Implementation for EPUB Files (2:22)
Working with PDF Files (4:21)
PDF Setup for RAG (5:51)
RAG Implementation for PDF Files (5:37)
What Did You Learn in This Section? (3:56)
Exercise: Imposter Syndrome (2:55)
Section 12: Multimodal RAG
Overview: Multimodal RAG (3:38)
Introduction to Multimodal RAG (5:58)
Setup and Video Processing (5:23)
Extracting Audio from Video (8:44)
Compressing Audio Files (4:17)
Transcribing Audio with OpenAI Whisper (10:07)
Whisper Model (6:31)
Extracting Frames from Video (5:49)
Introduction to Contrastive Learning (5:14)
Understanding the CLIP Model (5:22)
Tokenizing Text for Multimodal Tasks (8:13)
Chunking and Embedding Text (11:36)
Embedding Images for Multimodal Analysis (8:36)
Understanding Cosine Similarity in Multimodal Contexts (6:46)
Applying Contrastive Learning and Cosine Similarity (10:26)
Visualizing Text and Image Embeddings (11:11)
Query Embedding Techniques (4:12)
Calculating Cosine Similarity for Query and Text (11:47)
GenAI Model Setup for Multimodal Tasks (4:55)
Building a GenAI Model (7:11)
What Did You Learn in This Section? (2:12)
Section 13: Project - Starbucks Financial Data
Project Briefing: Starbucks Financial Data (5:27)
Transcribing Audio with OpenAI Whisper (11:22)
Embedding Transcription with CLIP (7:35)
Converting PDF to Images (5:57)
Embedding Images for Multimodal Analysis (4:58)
Retrieval System (17:13)
Preparing Context (4:59)
Generative System (12:46)
Section 14: Agentic RAG
Overview: Agentic RAG (2:51)
AI Agents (7:51)
Agentic RAG (5:44)
Setup and Data Loading (9:54)
State Management and Memory in Agentic Systems (7:54)
AgentState Class (4:29)
Greeting the Customer (4:52)
AI Agent that Checks the Question (10:47)
AI Agent that Assesses the Validity of the question (7:22)
Retrieving the Documents (5:46)
Testing the App (7:13)
Generate Answers (9:21)
AI Agent that Improves the Answer (11:13)
Asking User For More Questions (5:29)
Agentic RAG Recap - Key Learnings and Next Steps (6:17)
Where To Go From Here?
Thank You! (1:17)
Review This Course!
Become An Alumni
Learning Guideline
ZTM Events Every Month
LinkedIn Endorsements
AI Agent that Assesses the Validity of the question
This lecture is available exclusively for ZTM Academy members.
If you're already a member,
you'll need to login
.
Join ZTM To Unlock All Lectures