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AI & LLM Engineering Mastery - GenAI, RAG Complete Guide
AI & LLM Engineering Mastery - GenAI, RAG Complete Guide

AI & LLM Engineering Mastery - GenAI, RAG Complete Guide: Master Generative AI, Transformers, LangChain & RAG from Scratch to Deployment

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Profile Icon Paulo Dichone
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€8.99 €82.99
Video Jun 2025 18hrs 16mins 1st Edition
Video
€8.99 €82.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Paulo Dichone
Arrow right icon
€8.99 €82.99
Video Jun 2025 18hrs 16mins 1st Edition
Video
€8.99 €82.99
Subscription
Free Trial
Renews at $19.99p/m
Video
€8.99 €82.99
Subscription
Free Trial
Renews at $19.99p/m

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Key benefits

  • Complete coverage of GenAI, LLMs, RAG, and fine-tuning with real-world projects
  • Practical hands-on coding with Transformers, OpenAI, and LangChain frameworks
  • In-depth training on Prompt Engineering, memory, and API deployment techniques

Description

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.

Who is this book for?

This course is ideal for software developers, ML engineers, data scientists, and tech enthusiasts eager to master GenAI and LLM applications. A basic understanding of programming is helpful, though the optional Python section ensures accessibility to beginners.

What you will learn

  • Build real-world LLM apps using LangChain and Transformers
  • Fine-tune models with OpenAI and implement LoRA techniques
  • Design and deploy RAG pipelines with PDF and UI integration
  • Implement memory and context handling in LLM applications
  • Engineer effective prompts using advanced prompting methods
  • Create and test production-ready AI APIs for custom models

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 05, 2025
Length: 18hrs 16mins
Edition : 1st
Language : English
ISBN-13 : 9781806104758
Category :
Concepts :

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Product Details

Publication date : Jun 05, 2025
Length: 18hrs 16mins
Edition : 1st
Language : English
ISBN-13 : 9781806104758
Category :
Concepts :

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Table of Contents

18 Chapters
Introduction Chevron down icon Chevron up icon
Development Environment Setup Chevron down icon Chevron up icon
Optional: Python Deep Dive—Master Python Fundamentals Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Generative AI: Architecture and Core Technologies Chevron down icon Chevron up icon
LLMs: Concepts, Architecture, and Hands-On Development Chevron down icon Chevron up icon
OpenAI Models and Setup Chevron down icon Chevron up icon
Prompt Engineering: From Basics to Advanced Chevron down icon Chevron up icon
Context and Memory Management in LLMs Chevron down icon Chevron up icon
Logging in LLM Applications Chevron down icon Chevron up icon
Understanding Retrieval-Augmented Generation (RAG) Chevron down icon Chevron up icon
RAG PDF Workflow and UI Integration Chevron down icon Chevron up icon
Hands-On: PDF RAG System with Text Chunking Chevron down icon Chevron up icon
LangChain Fundamentals and Workflow Integration Chevron down icon Chevron up icon
Hands-On: Building LLM Applications with LangChain Chevron down icon Chevron up icon
Fine-Tuning LLMs Chevron down icon Chevron up icon
LoRA-Based Fine-Tuning and Deployment Chevron down icon Chevron up icon
Wrap-Up and Next Steps Chevron down icon Chevron up icon
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