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
LLMs, Few-shot, Scaling and Factuality
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