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AI Engineering: RAG (Retrieval Augmented Generation) for LLMs
Section 1: Introduction to Retrieval Augmented Generation (RAG) Systems
Course Outline (3:10)
Exercise: Meet Your Classmates and Instructor
Meet Rubber Ducky! Your AI Course Assistant using RAG (6:04)
Link to Your AI Course Assistant
Understanding Your Video Player
[ACTION] Download the Course Resources
Set Your Learning Streak Goal
PART A: BASICS OF PROMPT ENGINEERING, PYTHON AND OPENAI API
Who Is This Part For? (2:15)
Section 2: Prompt Engineering Basics
Game Plan for Prompt Engineering Basics (4:30)
Setting Up the OpenAI API (3:03)
Few-Shot Prompting (3:10)
Few-Shot in Practice (9:53)
Role, Persona and Goal (4:58)
Role, Persona and Goal in Practice (4:45)
System Message (5:04)
System Message in Practice (6:01)
My Favourite Prompt (4:02)
Section 3: Understanding LLMs Part 1
Understanding Transformers (12:47)
Attention Mechanisms (6:14)
Section 4: Python for RAG and GenAI
Game Plan for Python for RAG and GenAI (1:34)
Loops (5:18)
Loops: Easy Level (8:31)
Loops: Medium Level - Part 1 (3:45)
Loops: Medium Level - Part 2 (3:55)
Loops: Hard Level (2:56)
Functions (4:43)
Functions: Easy Level - Part 1 (4:06)
Functions: Easy Level - Part 2 (1:30)
Functions: Medium Level - Part 1 (2:49)
Functions: Medium Level - Part 2 (3:07)
Functions: Hard Level (7:01)
Introduction to Classes (4:51)
Classes: Easy Level - Part 1 (10:29)
Classes: Easy Level - Part 2 (3:52)
Classes: Medium Level (8:42)
Section 5: Understanding LLMs Part 2
OpenAI Tokenizer (6:12)
Section 6: OpenAI API
Overview: Working with the OpenAI API (3:47)
OpenAI API for Text (4:52)
Setting Up OpenAI API Key (5:08)
OpenAI API (5:02)
Generating Text with OpenAI API (6:37)
OpenAI API Parameters (6:55)
OpenAI API for Images (4:51)
With Image URL (9:19)
With Image in Base64 (10:08)
Adding Few-Shot Prompting (6:26)
What Did You Learn in this Section? (3:50)
Section 7: Understanding LLMs Part 3
Playing the Dice, Rock, Paper, Scissors, and Guess the Number (8:11)
Section 8: CAPSTONE PROJECT: Deploy With Lovable
Claim Your Free Credits
Project Presentation: Build a LinkedIn Post Writer App (2:57)
UI Design via Image Generation (7:45)
Lovable Build Prompt (5:29)
Deploy on Lovable (11:50)
Course Check-In
PART B - RAG
What to Expect of Part B (4:32)
Section 9 - RAG with OpenAI File Search
OpenAI File Search (2:04)
Project Presentation: Build a Mini Rubber Ducky (3:43)
Vector Stores (4:05)
Setup (1:57)
Retrieving the Files Path (6:54)
File and Vector Stores in OpenAI (9:52)
Responses Endpoint with File Search (9:32)
Section 10 - Deploy RAG with Streamlit
Setting Up on Cursor and Requirements (5:35)
Building Your AI Web App (2:35)
Virtual Environment and .env File (8:43)
Configuring the Page (10:14)
Session State and Vector Store (8:06)
Start Building the App: Sidebar (5:43)
Building the App: Chat Inputs (5:06)
Building the App: Bot Common Kwargs (9:59)
Building the App: Bot Answers (9:36)
Building the App: System Instructions (5:57)
GitHub Repository (6:26)
Deploying to Streamlit (3:02)
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)
Initial Setup for Data Processing (10:46)
Loading Data (6:36)
Developing a Retrieval System for Unstructured Data (6:55)
Building a Generation System for Dynamic Content (3:38)
Building Retrieval and Generation Functions (9:04)
Working with Word Documents (4:54)
Setting Up Word Documents for RAG (11:48)
Working with PowerPoint Presentations (4:44)
PowerPoint Setup for RAG (5:32)
Working with EPUB Files (4:58)
EPUB Setup for RAG (4:15)
Working with PDF Files (4:21)
PDF Setup for RAG (9:55)
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: Knowledge Graphs with LightRAG
Game Plan for Knowledge Graphs with LightRAG (2:19)
Knowledge Graphs (7:19)
Knowledge Graphs vs Embeddings (8:49)
LightRAG Setup (5:55)
What is LightRAG? (4:40)
Setting the Working Directory (5:49)
Local RAG (8:40)
Knowledge Graph Visualization (12:16)
Global and Hybrid RAG (7:12)
Naive, Mix and Bypass RAG (3:35)
Section 15: Agentic RAG
Overview: Agentic RAG (2:51)
AI Agents (7:51)
Agentic RAG (5:44)
Setup, Data Loading and AgentState (6:49)
State Management and Memory in Agentic Systems (7:54)
Greeting The Customer (8:04)
AI Agent that Checks the Question (7:03)
AI Agent that Assesses the Validity of the Question (7:17)
AI Agent that Generates the Answer (12:19)
AI Agent that Improves the Answer (5:32)
Asking User for More Questions (11:21)
Testing and Improving Agentic RAG (5:41)
Agentic RAG Recap - Key Learnings and Next Steps (6:17)
Section 16: Deploy Agentic RAG with Vercel
Preparing the Prompt with ChatGPT or Gemini (18:14)
Game Plan for Deploying Agentic RAG (1:03)
UX Mock Ups with Stich (3:05)
Setting Up with Cursor (2:58)
Testing the App Locally (21:23)
Final Debugging (4:12)
Push to Github (4:41)
Deploying to Vercel (1:48)
Testing the App (4:10)
Section 17: RAGAS
Game Plan for RAGAS (1:53)
Assessing RAG with RAGAS (6:13)
RAGAS Setup (7:44)
RAG (5:23)
Synthetic Data (3:36)
Generating Synthetic Data (7:02)
Answering Synthetic Dataset (5:13)
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score (5:32)
ROUGE (13:49)
LLM-Based Assessment (6:07)
Simple Criteria Score - Part 1 (5:35)
Simple Criteria Score - Part 2 (5:39)
Factual Correctness (5:16)
Rubrics Score (4:52)
Semantic Similarity (4:46)
Factual Correctness (4:57)
Context Precision (3:12)
Semantic Similarity (6:21)
Context Recall (3:11)
Context Precision (5:57)
Response Relevancy (4:36)
Context Recall (4:55)
Response Relevancy (6:22)
Key Learnings and Outcomes: RAGAS (3:17)
Where To Go From Here?
Thank You! (1:17)
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Project Presentation: Build a Mini Rubber Ducky
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