This course explores the cutting-edge domain of Generative AI, focusing on Large Language Models (LLMs) such as OpenAI's GPT, Google’s PaLM, Meta’s LLaMA, and other transformer-based architectures. Learners begin by understanding the foundational principles of generative models—unsupervised learning, self-supervised pretraining, and autoregressive versus encoder-decoder configurations. The course explains how LLMs are trained on massive text corpora to learn statistical patterns and generate coherent, human-like text. Learners dive into architectures like GPT-3/4 and fine-tuning methods such as instruction tuning, RLHF (Reinforcement Learning with Human Feedback), and prompt engineering. Emphasis is placed on real-world use cases like content creation, code generation, chatbots, summarization, and knowledge retrieval. Practical exercises include working with APIs (OpenAI, Cohere, Hugging Face), building apps with LangChain, and integrating vector databases (like Pinecone or FAISS) for context-aware generation. Students learn to craft effective prompts, chain reasoning, and construct RAG (Retrieval-Augmented Generation) pipelines. The course also introduces concepts like hallucination detection, output filtering, and fine-tuning with custom datasets. Ethics and risks are discussed, including bias, misinformation, copyright concerns, and misuse of generative outputs. By the end, learners will be proficient in applying LLMs to solve real problems, build intelligent systems, and stay ahead in the rapidly evolving generative AI landscape.