Imagine training an AI agent to make perfect recommendations, like suggesting the ideal movie for every user. That's the promise of contextual multi-armed bandits (CMABs), a powerful algorithm used in personalization. But CMABs face a cold-start problem: they initially know nothing about user preferences and must learn through trial and error. This early learning phase can be slow and inefficient. New research explores how large language models (LLMs) can solve this conundrum. LLMs, trained on massive datasets of human language and preferences, can simulate human behavior remarkably well. Researchers have found that using an LLM to generate synthetic user preferences and then training a CMAB on this synthetic data can significantly jumpstart the learning process. This approach, called Contextual Bandits with LLM Initialization (CBLI), offers a clever workaround to the cold-start problem. By pre-training the bandit with LLM-generated data, the AI agent hits the ground running, armed with an initial understanding of user preferences. This reduces the need for extensive real-world data collection, which can be costly and raise privacy concerns. In experiments, CBLI consistently outperformed traditional cold-start bandits. In one scenario, a CMAB was trained to personalize email campaigns for charity donations. Pre-training the bandit with LLM-generated donor profiles and preferences led to substantially higher donation rates compared to a non-pretrained bandit. Another experiment used real-world data from a survey about COVID-19 vaccine preferences. Again, CBLI significantly improved the bandit's ability to predict which vaccine features mattered most to different people. While CBLI shows impressive potential, researchers acknowledge its limitations. LLMs can inherit biases from their training data, and their simulated preferences may not always perfectly match reality. Future research will focus on mitigating these biases and developing more robust pre-training methods. Despite these challenges, CBLI represents an exciting advancement in AI. By combining the strengths of LLMs and contextual bandits, it paves the way for more efficient, effective, and personalized AI systems.