Predictive modeling, a cornerstone of modern data science, often grapples with the challenge of limited data. Acquiring labeled data, especially in specialized fields like healthcare, can be expensive and time-consuming. This is where the power of Large Language Models (LLMs) comes into play. A new research paper explores how LLMs can be used to extract expert-level prior knowledge, boosting the accuracy of predictive models even with sparse datasets. Think of it like giving your AI a head start. Instead of starting from scratch, the model leverages the LLM's vast knowledge base to form initial assumptions about the data, similar to how a human expert would use their experience to guide their analysis. This innovative approach, called 'prior elicitation,' allows LLMs to provide informed guesses about the relationships between different variables in the data. These guesses, expressed as probability distributions, act as a compass for the predictive model, guiding it towards more accurate predictions. The study found that LLM-elicited priors significantly outperformed uninformative priors, leading to substantial reductions in predictive error. In one compelling example, using LLM-elicited priors for infection prediction reduced the number of required labels by a staggering 55% and achieved the same accuracy 200 days earlier in the study. This translates to faster insights and potentially life-saving interventions in real-world scenarios. This research also tackled the question of whether LLMs can truly reason like humans. By comparing LLM-elicited priors to the LLM's own internal predictions, the researchers discovered discrepancies. This suggests that while LLMs can provide valuable prior knowledge, they don't necessarily apply this knowledge consistently in their own reasoning. The future of predictive modeling may lie in combining the strengths of both LLMs and traditional statistical methods. By leveraging the breadth of knowledge encoded in LLMs, we can enhance the accuracy and efficiency of our predictive models, particularly when dealing with limited data. This approach opens doors to faster discoveries, reduced costs, and improved outcomes in various domains, from healthcare to finance and beyond. While challenges remain in ensuring fairness and extending this approach to more complex models like neural networks, the potential benefits are immense. This research is a significant step towards harnessing the full power of LLMs for practical, real-world applications.