Foundations of Large Language Models: Fine-Tuning and RAG

Large Language Models are transforming how we build intelligent applications, but understanding how to customize and deploy them effectively requires a solid grasp of core concepts. This text-based course guides you from foundational AI principles to advanced optimization techniques. You will transition from a curious learner to a practitioner capable of selecting, fine-tuning, and integrating open-source language models. By studying detailed conceptual breakdowns and code implementations, you will learn how to adapt models to specific business domains and control their outputs. What you'll learn: Understand the fundamental architecture of transformer-based language models and how they process text; Apply modern prompt engineering techniques to guide model outputs reliably; Fine-tune pre-trained models using parameter-efficient methods like LoRA and QLoRA; Configure Retrieval-Augmented Generation (RAG) pipelines using vector databases to ground model responses; Evaluate model performance and optimize inference speed for production scenarios. The course begins with essential terminology and the mechanics of tokenization, then moves progressively through hands-on fine-tuning strategies and modern RAG architectures. This course is designed for software developers, data enthusiasts, and technical beginners eager to build with generative AI; no prior machine learning experience is required, though basic Python familiarity is helpful. Start reading today to unlock the potential of custom language models.