According to God of Prompt on Twitter, integrating consequences into prompts for large language models (LLMs) leverages the model's training on human text, which is inherently shaped by real-world stakes and outcomes (Twitter Source). By emphasizing consequences, prompt engineers can trigger more contextually aware and impactful responses, as LLMs learn from data where instructions carry significant meaning. This technique presents an opportunity for businesses and developers to enhance AI-generated content quality and reliability, especially in critical applications like legal, healthcare, and enterprise solutions.