Welcome to the module LLM Training, Optimization, and Evaluation. In this module, you will dive deeper into how Large Language Models are trained, optimized, and assessed for performance and reliability. You will begin by understanding the fundamentals of LLM training and optimization, including how massive datasets and computational resources are used to build high-performing models. The module explores different learning techniques such as zero-shot, few-shot, instruction tuning, and Reinforcement Learning from Human Feedback (RLHF), helping you understand how models adapt to tasks with minimal examples. You will also learn about loss functions and how they guide model learning during training. The concept of LLM alignment is introduced to explain how models are tuned to produce safe, accurate, and human-aligned responses. On the evaluation side, you will examine key evaluation metrics, including perplexity, and understand how model quality is measured. The module highlights the critical role humans play in evaluating outputs and refining models, as well as the importance of GPUs in enabling large-scale model training. By the end of this module, you will have a strong understanding of how LLMs are trained, optimized, aligned, and evaluated in real-world AI systems.

Welcome to the module Prompt Engineering, Fine-Tuning, and Advanced LLM Architectures. In this module, you will focus on practical techniques for controlling, adapting, and enhancing Large Language Models to meet real-world requirements. You will start with Prompt Engineering, learning the fundamentals of prompt design and how prompt structure directly impacts model output. The module covers proven techniques for crafting effective prompts that improve accuracy, reasoning quality, and response consistency. A hands-on demo will help you see how small prompt changes can significantly influence LLM behavior. Next, you will explore LLM fine-tuning approaches, including prompt tuning and Parameter-Efficient Fine-Tuning (PEFT). You will understand how prompt-efficient methods such as P-Tuning adapt large models with minimal computational cost. The introduction to NVIDIA NeMo provides insight into frameworks used for customizing and optimizing enterprise-scale language models. Finally, you will examine Retrieval-Augmented Generation (RAG) architecture and learn how combining LLMs with external knowledge sources improves factual grounding and domain-specific performance. By the end of this module, you will understand how to design high-quality prompts, apply efficient fine-tuning techniques, and leverage advanced LLM architectures for scalable generative AI solutions.