According to God of Prompt, a systematic approach to prompt engineering using five distinct variations—direct questioning, role-based framing, contrarian angle, first principles analysis, and historical comparison—can significantly enhance the reasoning abilities of large language models (LLMs). Each variation encourages the LLM to approach the decision-making process from a unique perspective, which can result in more comprehensive and nuanced risk assessments. As reported by God of Prompt, this merging strategy holds practical value for AI industry professionals seeking to optimize LLM outputs for business analysis, risk identification, and decision support applications.

According to Greg Brockman (@gdb) citing Research_FRI, the latest forecasting benchmark reveals that current AI models, particularly GPT-4.5, are nearing the predictive performance of human superforecasters. With existing trends, AI models could match superforecaster accuracy within one year. This development highlights significant advancements in AI-driven decision support and risk prediction, opening new business opportunities for enterprises in finance, logistics, and strategic planning to leverage AI for forecasting applications. The rapid progress in benchmark results demonstrates the potential for AI to transform professional forecasting services and reshape competitive dynamics in industries reliant on predictive analytics (Source: x.com/Research_FRI/status/1975909516777537614, Oct 9, 2025).