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AI Engineering: Retrieval Augmented Generation (RAG) for LLMs

via Zero To Mastery

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Overview

Thrive in the AI era with this hands-on course that teaches you to build better AI applications using one of the most important AI techniques used in the real-world to supplement an AI model's knowledge with proprietary or new information: Retrieval Augmented Generation (RAG).

Combine generative AI models with Retrieval Augmented Generation to build smarter AI systems
Use OpenAI APIs for text generation and processing unstructured data
Master FAISS for efficient similarity searches in massive datasets
Apply prompt engineering techniques for optimal AI responses
Build real-world AI projects like chatbots and financial analysis tools
Explore advanced RAG concepts like multimodal and agentic RAG

Syllabus

Section 1: Introduction to Retrieval Augmented Generation (RAG) Systems

Course Outline
Exercise: Meet Your Classmates and Instructor
Meet Rubber Ducky! Your AI Course Assistant using RAG
Link to Your AI Course Assistant
Understanding Your Video Player
[ACTION] Download the Course Resources
Set Your Learning Streak Goal

Section 2: Basics of Prompt Engineering, Python and OpenAI API

Who Is This Part For?

Section 3: Prompt Engineering Basics

Game Plan for Prompt Engineering Basics
Setting Up the OpenAI API
Few-Shot Prompting
Few-Shot in Practice
Role, Persona and Goal
Role, Persona and Goal in Practice
System Message
System Message in Practice
My Favourite Prompt

Section 4: Understanding LLMs Part 1

Understanding Transformers
Attention Mechanisms

Section 5: Python for RAG and GenAI

Game Plan for Python for RAG and GenAI
Loops
Loops: Easy Level
Loops: Medium Level - Part 1
Loops: Medium Level - Part 2
Loops: Hard Level
Functions
Functions: Easy Level - Part 1
Functions: Easy Level - Part 2
Functions: Medium Level - Part 1
Functions: Medium Level - Part 2
Functions: Hard Level
Introduction to Classes
Classes: Easy Level - Part 1
Classes: Easy Level - Part 2
Classes: Medium Level

Section 6: Understanding LLMs Part 2

OpenAI Tokenizer

Section 7: OpenAI API

Overview: Working with the OpenAI API
OpenAI API for Text
Setting Up OpenAI API Key
OpenAI API
Generating Text with OpenAI API
OpenAI API Parameters
OpenAI API for Images
With Image URL
With Image in Base64
Adding Few-Shot Prompting
What Did You Learn in this Section?

Section 8: Understanding LLMs Part 3

Playing the Dice, Rock, Paper, Scissors, and Guess the Number

Section 9: CAPSTONE PROJECT: Deploy With Lovable

Claim Your Free Credits
Project Presentation: Build a LinkedIn Post Writer App
UI Design via Image Generation
Lovable Build Prompt
Deploy on Lovable
Course Check-In

Section 10: RAG with OpenAI GPT Models

Overview: RAG with OpenAI GPT Models
Case Study Briefing: Cooking Books
Converting PDF to Images
Reading a Single Image with GPT
Enhancing AI with Prompt Engineering
Reading All Images in a Dataset
Filtering Non-relevant Information
Understanding Embeddings in NLP
Generating Embeddings
Building FAISS Index and Metadata Integration
Implementing a Robust Retrieval System
Combining Outputs for Enhanced Results
Constructing a Generative Model
Complete RAG System Implementation
How to Improve RAG Systems Effectively?

Section 11: Working With Unstructured Data

Overview: Working With Unstructured Data
Introduction to Langchain Library
Excel Data: Best Practices for Data Handling
Python - Initial Setup for Data Processing
Loading Data and Implementing Chunking Strategies
Developing a Retrieval System for Unstructured Data
Building a Generation System for Dynamic Content
Building Retrieval and Generation Functions
Working with Word Documents
Setting Up Word Documents for RAG
Implementing RAG for Word Documents
Working with PowerPoint Presentations
PowerPoint Setup for RAG
RAG Implementation for PowerPoint
Working with EPUB Files
EPUB Setup for RAG
RAG Implementation for EPUB Files
Working with PDF Files
PDF Setup for RAG
RAG Implementation for PDF Files
What Did You Learn in This Section?
Exercise: Imposter Syndrome

Section 12: Multimodal RAG

Overview: Multimodal RAG
Introduction to Multimodal RAG
Setup and Video Processing
Extracting Audio from Video
Compressing Audio Files
Transcribing Audio with OpenAI Whisper
Whisper Model
Extracting Frames from Video
Introduction to Contrastive Learning
Understanding the CLIP Model
Tokenizing Text for Multimodal Tasks
Chunking and Embedding Text
Embedding Images for Multimodal Analysis
Understanding Cosine Similarity in Multimodal Contexts
Applying Contrastive Learning and Cosine Similarity
Visualizing Text and Image Embeddings
Query Embedding Techniques
Calculating Cosine Similarity for Query and Text
GenAI Model Setup for Multimodal Tasks
Building a GenAI Model
What Did You Learn in This Section?

Section 13: Project - Starbucks Financial Data

Project Briefing: Starbucks Financial Data
Transcribing Audio with OpenAI Whisper
Embedding Transcription with CLIP
Converting PDF to Images
Embedding Images for Multimodal Analysis
Retrieval System
Preparing Context
Generative System

Section 14: RAG with OpenAI File Search

RAG with OpenAI File Search
Vector Stores in OpenAI
Setting a Vector Store in the OpenAI API
Responses Endpoint with File Search
RAG with GPT-4.1-mini
RAG with System Developper / Messages

Section 15: Agentic RAG

Overview: Agentic RAG
AI Agents
Agentic RAG
Setup and Data Loading
State Management and Memory in Agentic Systems
AgentState Class
Greeting the Customer
AI Agent that Checks the Question
AI Agent that Assesses the Validity of the question
Retrieving the Documents
Testing the App
Generate Answers
AI Agent that Improves the Answer
Asking User For More Questions
Agentic RAG Recap - Key Learnings and Next Steps

Section 16: The Science of RAG

LongRAG and LightRAG

Section 17: Knowledge Graphs with LightRAG

Game Plan for Knowledge Graphs with LightRAG
Knowledge Graphs
Knowledge Graphs vs Embeddings
LightRAG library update (April 2025)
LightRAG Setup
What is LightRAG?
Setting the Working Directory
Data Prep
Naive RAG
Implementing LightRAG
Knowledge Graph Visualization
Local Knowledge Graph Visualization

Section 18: RAGAS

Game Plan for RAGAS
Assessing RAG with RAGAS
RAGAS Setup
Embedding and Facebook AI Similarity Search (FAISS)
Python - RAG
Synthetic Data
Generating Synthetic Data
Python - Answering Synthetic Dataset
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score
ROUGE
LLM-Based Assessment
Simple Criteria Score - Part 1
Simple Criteria Score - Part 2
Factual Correctness
Rubrics Score
Semantic Similarity
Factual Correctness
Context Precision
Semantic Similarity
Context Recall
Context Precision
Response Relevancy
Context Recall
Response Relevancy
Key Learnings and Outcomes: RAGAS

Where To Go From Here?

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LinkedIn Endorsements

Taught by

Diogo Resende

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