Developers, data professionals, students curious about AI, and career switchers entering the AI space
Python + GenAI
Build real AI applications with Python and LLMs
Learn Python from the ground up and use it to build real Generative AI applications — prompt engineering, working with LLM APIs, document processing, embeddings, semantic search, and RAG — ending with your own AI Knowledge Base Assistant.
Tools you'll use
Course overview
What this program will do for you.
The Python + GenAI program takes you from Python fundamentals to building production-ready Generative AI applications. You will first master Python — data structures, OOP, file handling, and APIs — then learn how large language models work, how to engineer reliable prompts, process documents, build embeddings-based semantic search, and create RAG pipelines. You will also use AI-assisted development tools like Claude Code and OpenAI Codex, and finish with real projects and a capstone AI Knowledge Base Assistant.
Final outcome
Build and deploy Generative AI applications with Python — from prompt engineering and LLM APIs to embeddings, semantic search, and RAG — with a solid understanding of how modern AI systems work.
Tools & Tech
Industry tools you will master.
Python
Programming
OpenAI API
LLM Provider
ChromaDB
Vector Database
Claude Code
AI-Assisted Dev
OpenAI Codex
AI-Assisted Dev
Requests
HTTP / APIs
Jupyter Notebook
Development
VS Code
Editor
Prerequisites
- No prior programming experience required
- Basic computer and internet literacy
- Enthusiasm for understanding how AI works
What's included
- ✓Live instructor-led sessions
- ✓Hands-on coding projects with real APIs
- ✓Access to OpenAI API credits guidance
- ✓Code repositories for every module
- ✓Certificate of Completion
- ✓Lifetime access to learning materials
Learning outcomes
Skills you will acquire.
Write clean, production-quality Python — data structures, OOP, and file handling
Work with JSON, REST APIs, and external libraries in Python
Understand how LLMs work — tokens, context windows, parameters, and limitations
Design reliable prompts and structured outputs for real AI tasks
Build embeddings-based semantic search with a vector database (ChromaDB)
Build RAG pipelines that answer questions grounded in your own documents
Use AI-assisted development tools like Claude Code and OpenAI Codex
Ship real AI apps — from a PDF chatbot to a full AI Knowledge Base Assistant
Ready to accelerate your career?
Join our next batch and start building real-world projects with industry experts. Live sessions, real datasets, and a certificate you can show employers.