| Course Name | Introduction To Generative AI |
| Course Code | 25ARE471 |
| Program | B. Tech. in Automation & Robotics Engineering |
Introduction to Generative AI Technology
Generative AI – Definition, Traditional AI Vs Generative AI, LLM – LLM Architecture, LLM Operations – Concept of Embedding, Tokenisation, Attention Mechanism. LLM Specifications, Overview of Multimodal Generative Models. Responsible AI principles – Bias, Fairness, and Explainability. Guideline on Generative AI usage.
Generative AI Tools & Applications
Generative AI Tools: Prompt Engineering – Role-Task-Output Format, Zero & Few shot learning, Chain of Thoughts, Chunking. Retrieval Augmented Generation (RAG) – Vector Embeddings, Semantic and Similarity Search. Basics of fine-tuning.
Applications: Text Processing – Summarisation, Q&A, Translation, Correction. Code Processing – Code generation in specific programming language or framework, Code migration, Code Inferencing. Image Processing – Image generation, Image analysis, Image inferencing
Advances in Generative AI Technology
AI Agents – Agentic Flow, Multi-agent systems. AI based autonomous systems – Self-Operating Computers. AI augmented workflow – Coding assistance. AI Management – Concept of LLMOps & Hyper-scalars, AI as System – Human-AI Interactions (HAX)
Course Objectives
Course Outcomes
At the end of the course, the student will be able to
CO1: Understand the concept of generation AI and its various tools
CO2: Practice AI principles and ethics that guide individuals towards responsible, fair development and use of AI
CO3: Apply the generative AI tools for different applications related to Automation and Robotics
CO4: Understand various advanced concepts of generative AI
CO-PO Mappings
| CO/PO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | PSO3 |
| CO1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||||||
| CO2 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | ||||||||
| CO3 | 2 | 2 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 3 | 3 | ||||
| CO4 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 |
| Assessment | Internal | End Semester |
| CA (Theory) | 30 | |
| Mid Semester Examination | 30 | |
| End Semester / Project | 40 |
*CA – Can be Quizzes, Assignments, Tutorials, Lab components and Reports
Text Books
‘A Beginner’s Guide to Generative AI An Introductory Path to Diffusion Models, ChatGPT, and LLMs’, Deepshikha Bhati, Springer Cham, 2025.
‘ Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs’ Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada, , Apress Berkeley, CA, Springer, 2023.
References
‘Introduction to Large Language Models’, Tanmoy Chakraborty, Wiley Wiley (25 December 2024); Wiley India Pvt Ltd. ISBN: 9789363864740, 484 pages
‘Foundations of Large Language Models’, Xiao T, Zhu J., arXiv preprint arXiv:2501.09223. 2025 Jan 16.
‘Introduction to Generative AI’, Dhamani, Numa., United States: Manning, 2024.
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