Large language models (LLMs) are impressive, but they're not perfect. One area where they often struggle is providing nuanced responses tailored to specific needs. Imagine an LLM giving overly technical medical advice on a public forum or avoiding financial details when an expert opinion is required. This lack of fine-grained control over LLM outputs poses a challenge, especially when dealing with sensitive domains like medicine, finance, and law. New research tackles this problem with an innovative inference-time alignment method. Instead of retraining the entire model for each new preference—a costly and time-consuming process—this technique learns encoded representations of preference dimensions called Alignment Vectors (AVs). Think of these AVs as adjustable knobs that control the 'expertise level' of the LLM's response. Want a generic answer? Turn the knob down. Need an expert opinion? Turn it up. Researchers tested this method by creating a synthetic dataset with queries and responses across three domains: medical, financial, and legal. Each query was paired with responses at three levels: avoidance, generic, and expert. By adjusting the AVs, they could dynamically fine-tune the LLM to produce the desired response level without retraining the entire model. The results are promising. This method allows for nuanced control over response proficiency, saving significant time and resources. Furthermore, it generalizes across domains. Fine-tuning the model for medical expertise, for example, also improved its performance in financial and legal domains. While promising, there are limitations. The current method relies on a grid search for multi-domain alignment, which can be computationally intensive. Future research will focus on streamlining this process and exploring more sophisticated techniques for obtaining alignment vectors. This research opens exciting new possibilities for controlling LLM behavior, paving the way for more tailored and reliable AI assistants in various specialized fields.