According to @godofprompt, leading AI labs implement a hierarchical context window optimization framework to enhance model performance. Instead of providing indiscriminate input, these labs structure data into three tiers: critical information (top 20%) including task and constraints, supporting data (middle 60%) such as examples and context, and reference materials (bottom 20%) like background info. Notably, AI models assign three times more weight to the first 25% of the context window compared to the last 25%, making the positioning of information crucial for optimized results. As reported by @godofprompt, this approach is widely adopted for boosting the accuracy and reliability of AI model outputs, offering actionable strategies for developers and enterprises to maximize business value from large language models.