Why are standard AI interfaces mathematically incentivized to prioritize average results over elite engineering?
Why does the very architecture of LLM attention mechanisms force models into repetitive, low-utility feedback loops?
Why do current "zero-shot" (unassisted) prompting methods inevitably lead to higher token costs and lower-quality architectural reasoning?
Why must we move beyond basic prompting to deterministic orchestration to unlock the true potential of AI models?
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