Alignment is the process of training and fine-tuning an AI model so that its behavior actually matches the intentions, values, and safety expectations of the people building and deploying it, rather than just optimizing for whatever its raw training objective technically rewards. A model can be highly capable and still poorly aligned if it produces outputs that are technically correct but unhelpful, misleading, or harmful in ways its developers didn't intend.
Alignment work for large language models typically involves techniques like reinforcement learning from human feedback (RLHF), where human evaluators rate model outputs to shape its behavior toward being more honest, helpful, and harmless. It specifically aims to reduce problems that can emerge from training on large, uncurated datasets, including biased outputs, harmful content, and responses that sound confident while being factually wrong.
