1. Hands-On Large Language Models: Language Understanding and Generation By: Jay Alammar,
2. Maarten Grootendorst |
   Large Language Models: A Deep Dive Bridging Theory and Practice By Uday Kamath et al
3. Al Engineering by Chip Huyen
4. Various technical blogs

This course provides hands-on engineering of Large Language Models (LLMs), focusing on the challenges of building, optimizing, and deploying them. Students will cover the entire lifecycle from: Transformer foundations, GPU scaling,Inference optimization,Fine-tuning Retrieval-augmented generation (RAG) Agentic tool use,Deployment on edge devices, Students will learn through real-world case studies, labs, and projects. By the end, students will be equipped to design production-grade LLM systems for both research and industry applications.

B.E/B.Tech/ M.Sc . (C omputer Scienc e)/ MCA

Basics of NLP , Deep Learning, Python Programming

Industry professionals/engineers who want to specialize in LLM engineering. Will be useful for – startups, govt institutes looking into AI adaption

Transformer Foundations, GPUs, Pretraining basics, inference optimization, quantization, fine-tuning, instruction tuning and alignment, reasoning, and alignment, retrieval-augmented generation, tool-use and agents, multimodal LLMs, evaluation and edge deployment.