UNIT I

INTRODUCTION TO GENERATIVE AI

Basics of Generative AI ൓ Evolution and key applications ൓ GenAI in engineering, industry, and automation ൓ Opportunities and challenges ൓ Ethical considerations ൓ Overview of LLMs, multimodal models, and agent concepts

UNIT II

NEURAL NETWORK FOUNDATIONS FOR GENERATION

Overview of neural networks ൓ Autoencoders and Variational Autoencoders (VAEs) ൓ Generative Adversarial Networks (GANs): architecture and intuition ൓ Comparison of reconstructive and adversarial generation ൓ Simple latent-space visualization

UNIT III

LARGE LANGUAGE MODELS (LLMs), RAG, AND MCP

Transformer fundamentals ൓ BERT, GPT family ൓ Prompt engineering ൓ Basic fine-tuning concepts ൓ Retrieval-Augmented Generation (RAG): embeddings, search, and context retrieval ൓ Introduction to Model Context Protocol (MCP) for t**l use

UNIT IV

MULTIMODAL GENERATION AND AGENTIC AI

Sequence models: RNN, LSTM for generative tasks ൓ Text൓image generation൓Introduction to AI Agents: planning loops, t**l invocation, RAG-enabled agents ൓ Simple multimodal demo.

UNIT V

EXPLAINABLE AIAND APPLICATIONS

Explainability techniques (LIME, SHAP) ൓ Fairness, Safety, Transparency ൓ Responsible Deployment of Generative Systems ൓ Hallucinationand error analysis in RAG/LLMs ൓ Introduction to regulatory and ethical frameworks ൓ Application of GenAI(PCB Design Optimization, Semiconductor Chip Architecture Generation, Code Generation for ECU) ൓ Application of Agentic AI (Engineering Bill of Materials (BoM) Generation, Autonomous Design Assistance, Noise Removal in Signals)