AI Alignment Fundamentals: Guide to Safe Large Language Models

As artificial intelligence systems become more capable, ensuring they act in accordance with human values, intentions, and safety standards is one of the most critical challenges of our time. This text-based course introduces you to the core principles of AI alignment, explaining how we guide large language models (LLMs) to be safe, reliable, and helpful. You will transition from a curious observer to someone who understands the technical and philosophical frameworks used to prevent AI hallucinations, bias, and harmful outputs. What you'll learn: 1. Understand the fundamental alignment problem and why it matters for modern AI systems. 2. Explore the core pillars of alignment: helpfulness, honesty, and harmlessness. 3. Learn how techniques like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) shape model behavior. 4. Identify common LLM risks, including hallucinations and jailbreaking, and how alignment mitigates them. 5. Examine modern paradigms such as Constitutional AI and automated red-teaming. The course begins with foundational definitions of AI safety before walking you through the practical methodologies and modern techniques used to secure these models. This introductory course is designed for tech enthusiasts, policy advocates, and absolute beginners who want to understand AI safety without needing a background in programming. Start reading today to build a strong foundation in the essential field of AI alignment.