Imagine teaching an AI your preferences without endless explanations. New research into "few-shot steerable alignment" explores how large language models (LLMs) can be personalized to individual users with minimal examples. Currently, aligning LLMs with human values involves techniques like reinforcement learning from human feedback (RLHF), which often assumes everyone wants the same thing. But human preferences are diverse and complex. This research tackles this challenge by inferring a user's underlying preferences from just a small sample of their choices. The key innovation lies in extending the Bradley-Terry-Luce (BTL) model, a common method for modeling preferences, to handle heterogeneous preferences by incorporating unobserved variability factors. This new framework uses a clever method called "functional parameter-space conditioning." This allows the LLM to adapt to individual preferences *at inference time*, meaning the model can adjust its behavior on the fly without needing retraining. Experiments show that this technique effectively captures and aligns with diverse preferences, offering a promising path towards truly personalized AI. While the research currently focuses on smaller-scale models and datasets, the implications are vast. Imagine AI assistants that perfectly understand your writing style, or chatbots that cater to your specific humor. The ability to quickly adapt LLMs to individual needs could revolutionize how we interact with AI, making it more intuitive and user-centric than ever before. Future research will explore more efficient methods for larger models and tackle the complexities of real-world preference data. The path towards personalized AI is just beginning, but this research shows a promising way forward.