Fine-Tuning Large Language Models for Generative AI

General pre-trained AI models often lack the specialized knowledge required for niche business applications. Fine-tuning is the essential process that transforms a generic large language model (LLM) into a tailored expert for your specific domain. This course guides you through the process of customizing causal LLMs. You will transition from understanding core model architectures to preparing instruction datasets and applying modern, resource-efficient fine-tuning methods that achieve high-accuracy results without requiring massive computing power. What you'll learn: - Understand the core architecture of causal large language models and how they generate text - Prepare and format high-quality instruction datasets for custom domain tasks - Apply Parameter-Efficient Fine-Tuning (PEFT) methods, including Low-Rank Adaptation (LoRA) - Configure training parameters to optimize model performance and prevent overfitting - Evaluate fine-tuned models using standard language processing metrics and benchmarks The course begins with foundational definitions of generative AI and model training before moving step-by-step through dataset preparation, fine-tuning configuration, and model evaluation. Designed for aspiring AI engineers, software developers, and tech enthusiasts, this course is accessible to beginners with no prior machine learning experience. Start reading today to build your practical understanding of modern LLM customization.