In this course, learners will develop a comprehensive and technically grounded understanding of generative AI — from foundational concepts through to hands-on model fine-tuning, retrieval-augmented generation, AI-assisted development, and AI-powered testing. Learners will explore generative AI, GPT models, machine learning, deep learning, and prompt engineering, applying responsible AI principles throughout. They will examine large language models, transformer architecture, and Hugging Face, and implement neural networks using PyTorch. Learners will fine-tune language models, build retrieval-augmented generation (RAG) applications, and apply advanced RAG techniques. They will use AI-assisted development tools including Claude Code and Codex CLI to build applications, and develop API testing skills with Postman covering RESTful APIs, variables, data files, end-to-end testing, and mock servers. The course concludes with a practical AI-assisted testing track covering front-end and back-end testing strategies, debugging, and production-ready testing using AI tools.