RAG, AI Agents and Generative AI with Python and OpenAI 2025 from Udemy

Welcome to "RAG, AI Agents, and Generative AI with Python and OpenAI 2025"—the ultimate course to master Retrieval-Augmented Generation (RAG), AI Agents, and Generative AI using Python and OpenAI's cutting-edge technologies.

If you aspire to become a leader in artificial intelligence, machine learning, and natural language processing, this is the course you've been waiting for.

Why Choose This Course?

Comprehensive Curriculum: Dive deep into RAG systems, AI agents, and generative AI models with over 150 lectures and 10 extensive sections.
Hands-On Python Projects: Implement real-world applications using Python, OpenAI GPT models, FAISS, LangChain, and more.
Latest Technologies: Stay ahead with the most recent advancements in OpenAI, Generative AI, Multimodal RAG, and AI Agents.
Expert Instruction: Learn from Diogo, an industry expert with years of experience in AI and machine learning.
Unlimited Updates: Get access to course enhancements and updates through 2025.

About Your Instructor

Hi, I'm Diogo, a data expert with a Master's degree in Management specializing in Analytics from ESMT Berlin.

With extensive experience tackling complex business challenges—from managing billion-euro sales planning to conducting A/B tests that led to significant investments—I bring real-world expertise to this course.

As a startup founder helping restaurants worldwide optimize menus and pricing through data insights, I'm passionate about leveraging AI for practical solutions.

Personalized Support

One of the key benefits of this course is the direct access to me as your instructor.

I personally respond to all your questions within 24 hours.

No outsourced support—just personalized guidance to help you overcome challenges and advance your skills.

Continuous Improvements

I'm dedicated to keeping this course up-to-date with the latest advancements in AI.

Your feedback shapes the course—I'm always listening and ready to add new content that benefits your learning journey.

What You'll Learn

Fundamentals of Retrieval Systems: Understand tokenization, indexing, querying, and ranking in information retrieval.
Basics of Generation Models: Master text generation using GPT, transformers, and attention mechanisms.
Integration of Retrieval and Generation: Build RAG systems combining retrieval models with generative models.
Advanced OpenAI GPT Models: Leverage OpenAI's GPT models for powerful text generation and embeddings.
Handling Unstructured Data: Work with data in various formats like Excel, Word, PowerPoint, EPUB, and PDF using LangChain.
Multimodal RAG: Explore multimodal retrieval systems incorporating text, audio, and visual data using Whisper and CLIP models.
AI Agents and Agentic RAG: Develop AI agents with state management and memory for complex tasks using OpenAI Swarm.
Capstone Projects: Apply your knowledge in real-world scenarios, including analyzing Starbucks financial data and building robust retrieval systems.

Why Master RAG and AI Agents Now?

The future of AI lies in systems that can retrieve relevant information and generate intelligent responses—Retrieval-Augmented Generation is at the forefront of this revolution.

By mastering RAG, AI agents, and generative models, you position yourself at the cutting edge of technology, making you invaluable in today's tech landscape.

Get Started Today.

Lifetime Access: Enroll now and get lifetime access to all course materials and updates.
Interactive Learning: Engage with coding exercises, challenges, and real-world projects.
Support: Get your questions answered from me, Diogo, in less than 24 hours.
Certification: Receive a certificate upon completion to showcase your new skills.

Don't Miss Out.

The world of AI is advancing rapidly.

Stay ahead of the curve by enrolling in "RAG, AI Agents, and Generative AI with Python and OpenAI 2025" today. Unlock endless possibilities in AI and machine learning.

Enroll Now and transform your career with the most comprehensive RAG and Generative AI course available.

What's inside

Learning objectives

Build retrieval-augmented generation (rag) systems using python and openai.
Develop ai agents with state management and memory using openai swarm.
Master generative ai models like openai gpts for text generation.
Leverage faiss and langchain for efficient retrieval systems.
Integrate multimodal rag using text, audio, and images with whisper and clip models.

Build real-world projects, including a capstone project analyzing financial data.
Stay ahead with the latest advancements in ai, generative ai, and ai agents in 2025.
Develop ai agents using crewai for advanced task automation and orchestration.
Deploy an agentic rag system with langgraph for a digital waiter.
Fine-tune gpt-4o models using python for customized ai solutions.

Build retrieval-augmented generation (rag) systems using python and openai.
Develop ai agents with state management and memory using openai swarm.
Master generative ai models like openai gpts for text generation.
Leverage faiss and langchain for efficient retrieval systems.
Integrate multimodal rag using text, audio, and images with whisper and clip models.
Build real-world projects, including a capstone project analyzing financial data.
Stay ahead with the latest advancements in ai, generative ai, and ai agents in 2025.
Develop ai agents using crewai for advanced task automation and orchestration.
Deploy an agentic rag system with langgraph for a digital waiter.
Fine-tune gpt-4o models using python for customized ai solutions.

Syllabus

RAG and Generative AI with Python

RAG, AI Agents and Generative AI with Python and OpenAI Promotional Video

Course Overview: RAG, AI Agents and Generative AI with Python and OpenAI

Your RAG, AI Agents and Generative AI Course Assistant is Here

RAG and Generative AI Course Assistant Link

Diogo's Introduction and Background

Get Your Course Materials: RAG and Generative AI

Unlimited Updates and Enhancements 2025

Submit Your Update and Enhancement Requests Here

Python Self Assessment - One Piece Edition

Python Self-Assessment Notes

Why take this self-assessment?

Gear Transformation Power Calculation

Solutions - Gear Transformation Power Calculation

Poneglyph Collection

Solutions - Poneglyph Collection

Devil Fruit User Class

Solutions - Devil Fruit User Class

Basics of Retrieval Systems for RAG and Generative AI

Game Plan for Fundamentals of Retrieval Systems

Overview of Information Retrieval

Understanding Tokenization in NLP

OpenAI Tokenizer

Haven't Downloaded the Course Materials?

Python - Libraries and Data Handling for RAG

Python - Tokenization Techniques

Python - Preprocessing Steps

Types of Retrieval Systems

Vector Space Model (TF-IDF)

Python - Implementing TF-IDF

Python - TF-IDF Function and Output Analysis

Boolean Retrieval Model

Python - Boolean Retrieval Implementation

Probabilistic Retrieval Model

Python - Probabilistic Retrieval Model

How Google Search Works?

Key Concepts: Indexing, Querying, and Ranking

Section Recap: Key Learnings

Scientific Literature Review - Prompt Engineering

Chain-of-Thought Prompt Engineering

ReAct Prompt Engineering

Basics of Generation Models for RAG

Game Plan for Basics of Generation Models

Introduction to Text Generation

Understanding Transformers

Rock-Paper-Scissors, Dices, and Strawberries

Python - Defining a Relevant Context Distance

Python - Text Generation with GPT-2

Python - Tokenization for Text Generation

Python - Padding the Data for Consistency

Attention Mechanisms in NLP

Python - Creating a Dataset Class

Python - Fine-Tuning the GPT-2 Model

Python - Generating Text with GPT-2

Basics of Generation Models Recap: Key Learnings

Scientific Literature Review - LLMs

LLMs, Few-shot, Scaling, and Factuality

Introduction to RAG

Game Plan for Integrating Retrieval and Generation

Introduction to RAG Architecture

Python - Tokenization and Embeddings for RAG

FAISS Index: Efficient Similarity Search

Python - Building a Retrieval System

Python - Developing a Generative Model

Python - Implementing the RAG System

Understanding Generation Model Parameters

Python - Configuring RAG with Parameters

What Have We Learned and Where Do We Go from Here?

Would you help me?

Scientific Literature Review - RAG

LongRAG and LightRAG

OpenAI API

Game Plan for OpenAI API

OpenAI API for Text

Python - Setting Up OpenAI API Key

System Message and Parameters

Python - OpenAI API Setup

Python - Generating Text with OpenAI API

Python - OpenAI API Parameters

OpenAI API for Images

Python - With Image URL

Python - Converting Images to Base64

Python - Assess My Python Course Thumbnail

Key Learnings and Outcomes: OpenAI API

CAPSTONE PROJECT: GenAI for Customer Acquisition

Project Presentation: GenAI for Customer Acquisition

Note about the next video

Python - OpenAI Setup

Python -.AI Agent System Prompt

Python - Processing Images for GenAI

Python - Extract Data with GenAI

Python - Improving GenAI Extraction

Python - GenAI with All Images

Python - PDF to Images

Python - Wrapping Up the OpenAI GenAI Project

RAG with OpenAI GPT Models

Game Plan for RAG with OpenAI Integration

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Focuses on Retrieval-Augmented Generation (RAG), AI Agents, and Generative AI, which are in demand for creating intelligent systems that can retrieve information and generate responses

Employs Python, OpenAI GPT models, FAISS, and LangChain, which are valuable tools for implementing real-world AI applications and staying ahead in the tech landscape

Covers handling unstructured data in various formats like Excel, Word, PowerPoint, EPUB, and PDF using LangChain, which is essential for practical AI solutions

Explores multimodal retrieval systems incorporating text, audio, and visual data using Whisper and CLIP models, which allows learners to work with diverse data types

Includes a capstone project analyzing financial data, which allows learners to apply their knowledge in real-world scenarios and build robust retrieval systems

Requires learners to set up an OpenAI API key, which may involve costs depending on usage and could be a barrier for some students

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in RAG, AI Agents and Generative AI with Python and OpenAI 2025 with these activities:

Review Python Fundamentals

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Reinforce your understanding of Python syntax, data structures, and basic programming concepts to ensure a smooth learning experience in the course.

Browse courses on Python Basics

Show steps

Review Python syntax and data types.
Practice writing basic Python functions.
Work through introductory Python tutorials.

Read 'Generative AI with Python and TensorFlow 2'

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Expand your knowledge of generative AI models and techniques that can be used in RAG systems.

View Generative AI with Python and TensorFlow 2:... on Amazon

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Read the chapters on transformers and generative models.
Experiment with the TensorFlow code examples.
Adapt the code examples to your own projects.

Read 'Natural Language Processing with Python'

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Gain a deeper understanding of NLP concepts and techniques that are foundational to RAG systems and generative AI.

View Natural Language Processing with Python:... on Amazon

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Read the chapters on tokenization and text processing.
Experiment with the NLTK library for text analysis.
Complete the exercises at the end of each chapter.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Follow LangChain Tutorials

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Enhance your practical skills by following LangChain tutorials to build and experiment with RAG systems and AI agents.

Show steps

Explore the LangChain documentation and tutorials.
Choose a tutorial that focuses on RAG or AI agents.
Follow the tutorial step-by-step and experiment with different parameters.
Adapt the tutorial to your own use case or dataset.

Build a Simple Question Answering System

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Apply your knowledge of retrieval systems and generative models by building a basic question answering system using Python and relevant libraries.

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Choose a dataset of text documents.
Implement a retrieval system using TF-IDF or vector embeddings.
Integrate a generative model to answer questions based on retrieved documents.
Evaluate the performance of your question answering system.

Create a Blog Post on RAG Architectures

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Solidify your understanding of RAG architectures by writing a blog post explaining the different components and their interactions.

Show steps

Research different RAG architectures and their use cases.
Outline the key components of a RAG system.
Write a clear and concise blog post explaining the architecture.
Include diagrams and examples to illustrate the concepts.

Contribute to a RAG-related Open Source Project

Show steps

Deepen your understanding and contribute to the community by contributing to an open-source project related to RAG or AI agents.

Show steps

Find an open-source project related to RAG or AI agents on GitHub.
Review the project's documentation and contribution guidelines.
Identify a bug or feature that you can contribute to.
Submit a pull request with your changes.

Career center

Learners who complete RAG, AI Agents and Generative AI with Python and OpenAI 2025 will develop knowledge and skills that may be useful to these careers:

Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs and implements AI systems, often working with natural language processing and machine learning. This course, with its focus on Retrieval-Augmented Generation, AI agents, and generative models, directly aligns with the core skills needed for this role. This course can help an AI Engineer build a strong foundation in developing systems that combine information retrieval with intelligent response generation. The hands-on Python projects, using OpenAI GPT models and LangChain, provide essential practical experience for an AI Engineer seeking to innovate in the field.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models and systems, often working on tasks that require text generation and information retrieval. The course's detailed exploration of generative AI models, including transformers and attention mechanisms, directly supports the ML Engineer's work. Because the course covers both retrieval and generation techniques and uses Python, this course helps build expertise in developing advanced RAG systems. The inclusion of multimodal RAG may also give this engineer a competitive edge in the job market.

See salaries and explore the career path for Machine Learning Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer focuses on enabling machines to understand and generate human language, working heavily with models that process text. This course's in-depth coverage of text generation using GPT, transformers, and attention mechanisms is directly applicable. This course helps an NLP Engineer build competency in using RAG systems effectively to combine retrieval models with generative text models. Further, the hands-on experience with tools such as LangChain makes this course especially relevant to the work of an NLP Engineer.

See salaries and explore the career path for Natural Language Processing Engineer

Generative AI Specialist

A Generative AI Specialist focuses on creating new content, such as text, images, or other media through artificial intelligence. This course provides a deep dive into generative models, especially text generation, using tools like OpenAI GPT. This specialist will find that the course helps build a foundation in using these models to create novel outputs. Further, the projects will help this specialist develop the practical skills that are needed for success in this role. This course is ideal for those seeking to be at the cutting edge of generative technologies.

See salaries and explore the career path for Generative AI Specialist

AI Research Scientist

An AI Research Scientist conducts research to advance the field of artificial intelligence, often developing new algorithms and models for tasks like text generation and information retrieval. Though a research role typically requires a PhD, this course provides a practical understanding of RAG systems, AI agents, and generative models, which are all foundational for AI research. This course may be helpful to a researcher seeking to stay informed of the latest practical applications of AI techniques and looking to integrate some of the technologies covered in this course into their studies. The course's focus on multimodal RAG is also useful to AI Research Scientists who are often exploring the cutting edge.

See salaries and explore the career path for AI Research Scientist

Data Scientist

A Data Scientist uses data analysis and machine learning to extract insights and solve problems, often dealing with unstructured data. While not solely focused on data science, this course's work with handling unstructured data, using LangChain and other tools, is valuable to a Data Scientist. The techniques to build retrieval systems and integrate them with generative models may be useful to those wishing to expand their skill set. The hands-on projects, including analyzing financial data, will help a data scientist build the practical skills to apply these techniques in their analyses. This course may be useful to those data scientists aiming to incorporate AI into their projects.

See salaries and explore the career path for Data Scientist

Software Developer

A Software Developer creates and maintains software applications, and may work with a variety of tools and technologies, including Python. This course directly aligns with the programming requirements of a software developer by using Python as its lingua franca. This course helps a developer learn to build applications that incorporate RAG systems and generative AI. Because of the focus on practical, hands-on experience, a developer may find it particularly useful for incorporating AI into the applications they build. The course may be helpful to software developers seeking to expand their skills.

See salaries and explore the career path for Software Developer

Solutions Architect

A Solutions Architect designs and guides the implementation of technology solutions, often requiring a broad understanding of different technologies. This course, with its focus on RAG systems, AI agents, and generative models, is relevant to those who must understand the capabilities of the latest AI tools. A solutions architect may take this course to learn how the integration of different components can be brought together into a cohesive system. The practical examples, and the work on real-world projects, can inform the architect's approach to solutions that leverage AI. This course may be useful to a Solutions Architect seeking to broaden their understanding of AI.

See salaries and explore the career path for Solutions Architect

Technology Consultant

A Technology Consultant advises clients on how to use technology to improve their businesses. This course may be useful to consultants who need to understand the capabilities of RAG, AI agents, and generative AI. This course helps consultants stay up-to-date with the latest advancements in artificial intelligence. The practical nature of the course may also give consultants a valuable working definition of some cutting edge technologies. A consultant may find this course useful in expanding their expertise.

See salaries and explore the career path for Technology Consultant

Technical Lead

A Technical Lead manages and guides a team of engineers or developers on technical projects. This course, with its focus on practical skills and hands-on projects, may be helpful for the lead who is managing a team that is working on AI. This course helps team leads learn about Retrieval-Augmented Generation, AI agents, and generative models, which can help them guide their teams effectively. The variety of technologies covered also gives the lead a broad view of modern AI systems. This course may be useful for guiding the team but it may not expand technical skills.

See salaries and explore the career path for Technical Lead

Data Analyst

A Data Analyst interprets data to identify trends and insights, which can be useful in a variety of applications and projects. While this course does not focus on data analysis per se, the skills taught to extract information from various formats, including Excel and PDF, may be useful to analysts who work with those formats. The course may also have some relevance to a data analyst seeking to learn how to use AI tools, such as generative models and AI agents, when extracting and analyzing data. This course may be useful as an introduction to some AI concepts.

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Product Manager

A Product Manager defines and guides the development of a product, often requiring them to understand both the technical aspects and user needs. This course, with its focus on AI technologies, may be useful for providing context to a product manager with products that incorporate AI. They may find that this course helps them better understand what is possible with AI and to strategize product roadmaps. The product manager may find value in understanding some of the tools used by AI developers. This course may help with strategic direction.

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Technical Writer

A Technical Writer creates documentation and other content that explains technical concepts to various audiences. This course, focused on RAG, AI agents, and generative AI, allows the technical writer to learn about these topics, leading to more informed and precise content creation. This course may be useful in expanding a technical writer's domain knowledge. The clear instruction and hands-on exercises in this course may give the writer a solid base of knowledge in AI that might be used when documenting AI concepts. This course may be helpful in specialized writing.

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Business Analyst

A Business Analyst analyzes business processes and requirements to help organizations improve. Though this course is not directly focused on business analysis, the skills in data extraction and analysis may be useful. The course also introduces concepts of AI agents, RAG, and generative AI, which may be useful for automating some business processes. The business analyst may find that this course has value in introducing these concepts. This course may be useful as a means to expand one's knowledge of AI.

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Project Manager

A Project Manager oversees projects from initiation to completion, often handling the budget, timeline, and team. This course may expose a project manager to the rapidly evolving field of AI. While this course does not provide project management training, a manager who wishes to oversee projects related to RAG, AI agents or generative AI, may find some concepts useful. This course may be helpful for those seeking a basic understanding of AI concepts and terminology.

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RAG, AI Agents and Generative AI with Python and OpenAI 2025

What's inside

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Syllabus

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