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Full Stack Generative AI, Agentic AI & RAG with Python
Highest Rated
Rating: 4.6 out of 5(40 ratings)
225 students

Full Stack Generative AI, Agentic AI & RAG with Python

Master Agentic AI, Generative AI, Agents, RAG, Vector DBs, Hugging face, LLMs with LangGraph and LangChain
Highest Rated
Created byDurga Prasad Chodavarapu
Last updated 8/2026
English

What you'll learn

  • Learn to build intelligent AI agents and full-stack applications using Python, modern AI frameworks, APIs, and web technologies.
  • Understand Retrieval-Augmented Generation (RAG), embeddings, vector databases, document processing, and semantic search to create knowledge-aware AI systems..
  • Work with LLMs, prompt engineering, AI APIs, and Python libraries to create practical generative AI applications and automation workflows.
  • Integrate Agentic AI, RAG, Generative AI, databases, and full-stack components to develop production-oriented intelligent applications from end to end.

Explore related topics

Course content

6 sections • 26 lectures • 14h 57m total length

  • Introduction6:28

    Build a solid foundation in AI and AWS AI services. Master core concepts, machine learning basics, deep learning, generative AI, and responsible AI for real-world applications.

Requirements

  • You must have a passion for learning Agentic AI, Generative AI and RAG

Description

Full Stack Generative AI, Agentic AI & RAG with Python is a comprehensive, hands-on online course designed to help learners build modern AI-powered applications from the ground up using Python. This course brings together Generative AI, Large Language Models, Retrieval-Augmented Generation, Agentic AI, APIs, databases, and full-stack application development into one practical learning experience.

You will begin with the fundamentals of Generative AI and Large Language Models, then progress into prompt engineering, embeddings, vector databases, and RAG pipelines. Learn how to build intelligent applications that can retrieve relevant information from documents and knowledge bases before generating accurate, context-aware responses.

The course also introduces Agentic AI concepts, enabling you to create AI agents capable of reasoning, planning, using tools, interacting with APIs, maintaining context, and completing multi-step tasks. Using Python, you will learn how to integrate LLMs with real-world applications and develop intelligent workflows rather than simple chatbot interfaces.

On the full-stack development side, you will explore how to connect AI backends with APIs, databases, and user interfaces to create complete production-style applications. You will gain practical experience building AI chatbots, document-based question-answering systems, intelligent assistants, RAG applications, and agent-based solutions.

Whether you are a Python developer, software engineer, data professional, AI enthusiast, or someone looking to transition into Generative AI development, this course provides a practical pathway from fundamentals to advanced AI application development.

By the end of the course, you will have the knowledge and practical skills needed to design, develop, integrate, and deploy full-stack Generative AI, Agentic AI, and RAG applications using Python.

Who this course is for:

  • It is for those who want to master Agentic AI, Generative AI and RAG

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