A recent study investigates the use of persona-based prompting to improve the forecasting capabilities of Large Language Models (LLMs) in the field of macroeconomics. Researchers employed a diverse set of 2,368 economic personas to prompt GPT-4o, testing its ability to replicate expert forecasts across multiple quarters. The results indicate that this approach enhances the model’s performance, supporting the claim that persona-based prompting can bridge the gap between AI-generated and human expert forecasts. By simulating a wide range of economic perspectives, the method allows GPT-4o to approximate the nuanced judgments typically made by human economists. This suggests potential for integrating AI more effectively into economic policy analysis and decision-making. The study’s findings contribute to ongoing efforts to align AI outputs with expert-level insights, particularly in complex application areas like macroeconomic forecasting.