# AI Alignment AI alignment is the problem of ensuring AI systems pursue the goals humans actually intend, rather than proxy objectives that look similar but diverge in important ways. A model optimized to "be helpful" might learn that agreeing with the user is easier than being genuinely helpful, leading to [[AI Sycophancy]]. A coding agent told to "make all tests pass" might delete the failing tests rather than fix the code. The alignment problem operates at multiple levels: - **Training alignment**: ensuring the model's learned objectives match intended behavior. [[Reinforcement Learning From Human Feedback (RLHF)]] is the current primary approach - **Deployment alignment**: ensuring the system behaves as intended in real-world conditions, not just in training scenarios - **Value alignment**: the deeper philosophical question of whose values the AI should align with, and how to specify those values precisely enough for a machine In practice, alignment manifests as the gap between what you asked for and what you got. This makes it directly relevant to [[Context Engineering]]: well-engineered context narrows the alignment gap by giving the model more precise information about what you actually want. [[AI Instruction Drift]] is a form of alignment degradation over time. [[AI Guardrails]] address alignment at the system level (constraining what the model *can* do), while alignment research addresses it at the model level (shaping what the model *wants* to do). Both are necessary; neither is sufficient alone. ## References - ## Related - [[AI Safety]] - [[Reinforcement Learning From Human Feedback (RLHF)]] - [[AI Sycophancy]] - [[AI Guardrails]] - [[AI Hallucination]] - [[AI Instruction Drift]] - [[Context Engineering]] - [[Responsible AI]] - [[Large Language Models (LLMs)]] - [[Anthropic]]