LLM Alignment: Training Raw Text Models into Helpful AI Assistants

Raw large language models are incredibly powerful at predicting the next word, but they require careful instruction and alignment to become truly useful conversational partners. This course demystifies the exact processes used to transform raw text predictors into safe, helpful, and reliable AI assistants. Through clear, step-by-step written explanations, you will explore the entire pipeline of modern model alignment. You will transition from understanding basic pre-training concepts to grasping advanced alignment methodologies, enabling you to confidently discuss, evaluate, and implement model tuning strategies. What you'll learn: Understand the core differences between pre-training, supervised fine-tuning, and alignment; Explore how human feedback shapes model behavior through reinforcement learning; Learn modern alignment alternatives such as Direct Preference Optimization (DPO); Analyze safety guardrails and techniques used to prevent harmful model outputs; Practice designing high-quality instruction datasets for custom tuning workflows; Evaluate model performance and alignment success using standard industry benchmarks. The journey begins with foundational definitions of language modeling before moving systematically through dataset curation, supervised fine-tuning, preference optimization, and safety evaluation. This course is designed for beginners, developers, and product managers who want to understand how modern AI assistants are shaped, requiring no prior machine learning experience. Start reading today to master the core principles of large language model alignment.