Getting AI to do what we actually want, not just what we literally ask, is one of the biggest challenges in the field. These guides explore AI alignment, the discipline of making sure AI systems reliably follow human intentions, values, and goals. You will learn about core techniques like reinforcement learning from human feedback and constitutional AI that shape how modern chatbots and assistants behave. The topic also covers the alignment problem at a deeper level, including reward hacking, goal misspecification, and the difficulty of encoding complex human values into mathematical objectives. You will understand why alignment matters not just for researchers building frontier models, but for anyone deploying AI in high-stakes settings like healthcare, finance, or hiring. Whether you are evaluating the safety properties of an AI tool, setting organisational guardrails, or simply trying to understand why AI sometimes behaves in surprising ways, these guides give you the essential background to think clearly about alignment.
Understand the challenge of AI alignment. From goal specification to value learning—why ensuring AI does what we want is harder than it sounds.
Understand Reinforcement Learning from Human Feedback. How modern AI systems learn from human preferences to become more helpful, harmless, and honest.
Constitutional AI trains models to follow principles, self-critique, and revise harmful outputs without human feedback on every example.