LLM alignment

Picture a brilliant new hire who can write, code, and reason, but who takes every instruction painfully literally and has no sense of what you actually meant. Alignment is the work of closing that gap: shaping the model's behavior so it tracks human intent and values rather than just predicting plausible text. A pretrained model knows a great deal but has no goal of its own; alignment gives it the goal of being genuinely useful and trustworthy to the person in front of it.

In practice alignment is built in layers. Pretraining supplies raw capability, instruction tuning teaches the model to follow requests instead of merely continuing them, and preference-based methods such as RLHF refine tone, helpfulness, and safety using human judgements about which responses are better. The aim is a model that stays helpful, honest, and harmless even on prompts no one wrote a rule for.

Alignment is not a single fixed target. People and cultures value different things, and a model can look perfectly aligned on the cases you tested while behaving badly on novel ones. So alignment is best treated as ongoing measurement and correction, not a box you tick once and forget.