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Ultimate Generative AI and Agentic AI and RAG with Python
Highest Rated
Rating: 4.9 out of 5(47 ratings)
205 students

Ultimate Generative AI and Agentic AI and RAG with Python

Master Generative AI and Agentic AI and RAG with Python and Learn Agentic AI Design Patterns and More
Created bySatish Kothala
Last updated 10/2026
English

What you'll learn

  • Understand the core concepts of Generative AI, Large Language Models (LLMs), prompt engineering, and transformer architectures to develop intelligent AI applica
  • Learn to design and implement RAG pipelines using document processing, embeddings, vector databases
  • Build autonomous AI agents using Python, LLMs, tools, memory, and reasoning capabilities to automate complex workflows, execute multi-step tasks
  • Integrate Generative AI, Agentic AI, and RAG techniques to develop practical applications such as intelligent chatbots, AI assistants

Course content

80 sections • 400 lectures • 101h 2m total length
  • Introduction6:24

    Explore programming fundamentals from basics to mastering five languages—Python, Java, C, C++, and C#—with a focus on algorithms, logical thinking, debugging, and building real-world projects.

Requirements

  • You must be curious to learn Agentic AI and Generative AI

Description

The Ultimate Generative AI, Agentic AI, and RAG with Python course is designed to help learners develop practical skills in modern artificial intelligence and build intelligent AI-powered applications. This course provides a comprehensive introduction to Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI using Python programming. Whether you are a Python developer, software engineer, data scientist, AI enthusiast, or aspiring AI engineer, this course will help you understand essential AI concepts and apply them to real-world projects.

You will explore the fundamentals of Generative AI and learn how large language models generate human-like text, understand prompts, and support a wide range of applications. You will discover prompt engineering techniques, LLM integration, and methods for developing AI applications using Python. The course also introduces RAG architecture, including document processing, text chunking, embeddings, vector databases, semantic search, and context retrieval. You will learn how to connect language models with external knowledge sources to build AI systems that provide more relevant, context-aware responses.

Another important part of this course focuses on Agentic AI and autonomous AI agents. You will understand how AI agents use reasoning, tools, memory, and multi-step workflows to perform tasks with reduced manual intervention. You will explore how to develop AI agents using Python, integrate external tools and APIs, and design workflows that support automation and intelligent decision-making.

The course also covers practical approaches to combining Generative AI, RAG, and Agentic AI to create more capable AI solutions. You will learn how these technologies can work together to develop intelligent assistants, knowledge-based chatbots, document question-answering systems, research assistants, and automated business workflows.

By the end of this course, you will have a stronger understanding of modern AI engineering principles and the skills needed to develop Python-based AI applications. You will be better prepared to explore advanced AI projects, improve your programming capabilities, and pursue opportunities in Generative AI engineering, LLM application development, and Agentic AI development.

Who this course is for:

  • Anyone wanting to master Agentic AI and Generative AI from scratch