Optional: Python Deep Dive—Master Python Fundamentals | AI & LLM Engineering Mastery

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AI & LLM Engineering Mastery - GenAI, RAG Complete Guide

By : Paulo Dichone

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AI & LLM Engineering Mastery - GenAI, RAG Complete Guide

By: Paulo Dichone

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Overview of this book

This course is your launchpad into the fast-growing world of Generative AI and LLM application development. You'll begin by setting up a robust development environment and, if needed, reviewing Python fundamentals. From there, you'll dive into deep learning, machine learning, and neural network theory to build a strong conceptual foundation. Next, you'll shift to hands-on learning—exploring the architecture of LLMs and how Transformer models work. You'll develop real-world applications using Hugging Face’s Transformers and LangChain, while mastering Prompt Engineering, OpenAI API integration, memory management, and logging in AI-driven apps. Projects include chatbots, PDF RAG systems, news summarizers, and voice assistants designed for interactive, context-aware experiences. In the final chapters, you'll fine-tune LLMs using OpenAI tools and advanced techniques like LoRA. The course concludes by guiding you through the full lifecycle of AI application development—from concept to deployment as a production API. Whether you're starting out in GenAI or refining your LLM skills, this course equips you to build powerful AI solutions with confidence.

Introduction

Demo: What You'll Build in This Course

Course Structure

How to Get the Most from This Course

Development Environment Setup

Development Environment Setup: Overview

Install Python on Windows: For Windows Users

Install Python on Mac: For Mac Users

Download Visual Studio Code

Install the Python Extension Pack for VS Code

Running First Python Program in VS Code

Optional: Python Deep Dive—Master Python Fundamentals

What Is Python and Where It's Used?

Python Compilation and Interpretation Process

Declaring Variables in Python

Data Types

Python f-Strings

Numbers: Integers and Floats

Introduction to Lists: Accessing and Modifying Them

f-Strings and Individual Values from a List

Sorting a List and Getting a List Length

Lists and Loops: Looping Through a List

Making a List of Numbers with Loops and the Range Function

Statistics Functions for Numbers

Generate Even Numbers with the List and Range

Important: Code Organization Note

List Comprehension

Tuples

Branching: If Statements and Booleans

The Elif and the in Keywords

Hands-On: Using and and or Logical Operators

and or Logical Operators

Checking for Inequalities

Hands-On: Inner If-Statements

Data Structures: Dictionaries—Introduction and Declaring and Accessing Values

Modifying a Dictionary

Iterating Through a Dictionary

Nested Dictionaries and Looping Through Them

Looping Through a Dictionary with a List Inside

User Input and While Loops: User Input—Introduction

Hands-On: Odd or Even Number

While Loops and Simple Quit Program

Hands-On: Quiz Game

Removing All Instances of Specific Values from a List

Hands-On: Dream Travel Itinerary Program—Filling a Dictionary with User Input

Functions: Introduction

Passing Information to a Function (Parameters)

Positional and Named Arguments

Default Values: Parameters

Return Values from a Function

Hands-On: Returning an Integer and Intro to DocString

Functions: Passing a List as Argument

Passing an Arbitrary Number of Arguments to a Function

Introduction to Modules: Importing Specific Functions from a Module

Using the "as" as an Alias

Classes and OOP: Object-Oriented Programming—The init and str Methods

The init and str Methods

Adding More Methods to the Class

Setting a Default Value for an Attribute

Modifying Class Attribute: Directly and with Methods

Inheritance: Create an Ebook—Child Class

Overriding Methods

Creating and Importing from a Module

The Object Class: Overview

The Python Standard Library

Random Module: Random Fruit Hands-On

Hands-On: Random Fruit with Choice Module Method

Using Datetime Module

Writing and Reading Files: Do Useful Tasks with Python

The Path Class and Reading a Text File

Resolving Path: Reading from a Subdirectory with Path

Path Properties Overview

Writing to Text File with Path

Read and Write to File Using the "with" Keyword

Handling Exceptions

The "FileNotFound" and "IndexError" Exception Types

Custom Exception Creation and Handling

JSON: Reading and Writing to a JSON File

Hands-On: Writing and Reading Countries to JSON File

Hands-On: File Organizer

Python Virtual Environment and PIP

Setting Up Virtual Environment and Installing a Package

Hands-On: Watermarker Python Tool

Building an Image Watermarker in Python: Part 1

Generating the Watermarked Images

Reading CSV File: Introduction

Getting the CSV Header Position

Reading Data from a CSV Column

Plotting a Graph with CSV Data

Understanding Deep and Machine Learning

Deep and Machine Learning Deep Dive: Overview and Breakdown

Deep Learning Key Aspects

Deep Neural Network Dissection: Full Dive with Analogies

The Single Neuron Computation: Deep Dive

Weights: Deep Dive

Activation Functions: Deep Dive with Analogies

Deep Learning Summary

Machine Learning Introduction: Machine Learning vs. Deep Learning

Learning Types: Education System Analogy

Comparative Capabilities: Deep Learning, Machine Learning, and AI Summary

Generative AI: Architecture and Core Technologies

GenAI Introduction and Architecture Overview

GenAI Key Technologies: Limitations and Challenges

GenAI Key Components Overview and Summary

LLMs: Concepts, Architecture, and Hands-On Development

LLMs: Overview

The Transformer Architecture: Fundamentals

The Self-Attention Mechanism: Analogy

The Transformers Library: Deep Dive

Hands-On: Create a Simple LLM from the Transformers Library

Hands-On: Enhanced Transformers LLM

Open-Source vs. Closed-Source Models: Overview

OpenAI Models and Setup

Set Up OpenAI Account and API Key

Using APIs Effectively in AI Projects

Hands-On: Making Our First Call to OpenAI Model

Prompt Engineering: From Basics to Advanced

Prompt Engineering Introduction

Prompt Engineering and Types: Why It Matters

Hands-On: Simple Prompting Example

Advanced Prompting Techniques and Challenges

Hands-On: Few-Shots Prompting

Hands-On: Zero-Shot Prompting

Hands-On: Chain-of-Thought Prompting

Hands-On: Instructional Prompting

Hands-On: Role-Playing and Open-Ended Prompting

Temperature and Top-p Sampling

Hands-On: Prompt Techniques Combination and Streaming

Prompt Engineering Summary and Takeaways

Context and Memory Management in LLMs

Hands-On: Context and Memory Management Overview

What Is Context and Memory Management: Deep Dive

Hands-On: Adding Memory and Context to Chatbox

Summary

Logging in LLM Applications

Logging Introduction: What and the Why

Logging in LLM Applications and Logging Life Cycle

Hands-On: Chatbot with Logging

Summary

Understanding Retrieval-Augmented Generation (RAG)

RAG Introduction: What Is It?

RAG Key Components: The RAG Triad

RAG vs. Pure GenAI Models

RAG Deep Dive: Full Diagram Walkthrough

RAG Benefits and Practical Applications

RAG Challenges

RAG Fundamentals: Takeaways and Summary

RAG PDF Workflow and UI Integration

Building a RAG Pipeline: Overview

First RAG Workflow Architectural Diagram

Setting Up the Embedding Model Class

Hands-On: Building and Showcasing the RAG Workflow

Hands-On: RAG Workflow with UI—Streamlit

First RAG Pipeline Summary

Hands-On: PDF RAG System with Text Chunking

PDF RAG Workflow: Architecture Overview

PDF and Chunk Processing and Chunk Overlap: Deep Dive

Setting Up the SimpleRAGSystem Class and Methods

Testing the PDF RAG System

Simple PDF RAG Workflow: Summary

LangChain Fundamentals and Workflow Integration

LLM Frameworks Introduction: LangChain Fundamentals

What Is LangChain and Main Components

LangChain Setup and ChatModel

Hands-On: LangChain ChatPromptTemplates

Indexes, Retrievers, and Data Preparation: Overview

Hands-On: LangChain TextLoaders

Hands-On: Text Splitting and Cleaning

Hands-On: Embeddings and Retriever with FAISS VectorStore

LangChain TextSplitter: Deep Dive

LangChain DirectoryLoader

LangChain PDFLoader

Hands-On: LangChain Chains

Hands-On: Simple RAG System with Chat and LangChain Chains

Hands-On: Full RAG System QA Bot Using LangChain

Hands-On: Building LLM Applications with LangChain

LLM Application: News Summarizer—Architectural Overview

News Summarizer: Full Implementation

LLM Application: YouTube Video Summarizer—Architectural Overview

YouTube Video Summarizer and Q&A Dependency Setup

YouTube Video Summarizer Class Setup and Walkthrough

YouTube Video Summarizer Q&A: Testing the Workflow

LLM Application: Voice Assistant RAG System—Architectural Overview

Voice Assistant RAG System: Demo

Voice Assistant RAG System: Walkthrough and Demo

Fine-Tuning LLMs

Fine-Tuning Introduction: Overview

Fine-Tuning Techniques: Overview

Fine-Tuning Comparison of Techniques

Fine-Tuning General Process: Overview

Fine-Tuning OpenAI Models Pricing

Tokens and the Tokenizer OpenAI Tool

Hands-On: Fine-Tuning an OpenAI Model—Full Walkthrough

Creating a Chatbot with Our Fine-Tuned Model and Testing

LoRA-Based Fine-Tuning and Deployment

LoRA Introduction: Benefits

LoRA Deep Analysis

LoRA Implementation Strategy Workflow

Hands-On: Training Models—LoRA and PEFT

Running LoRA Model Fine-Tuning and Testing

Creating an API Service to Interface with Our Fine-Tuned Models

Testing Our LoRA Model API Endpoint

Chatting with LoRA Fine-Tuned Models

Full LoRA Workflow: Train and Chat with Fine-Tuned Models

Wrap-Up and Next Steps

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AI & LLM Engineering Mastery - GenAI, RAG Complete Guide

By : Paulo Dichone

AI & LLM Engineering Mastery - GenAI, RAG Complete Guide

By: Paulo Dichone

Overview of this book

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