As models reason better, the need for complex prompt chains diminishes.
Prompt engineering was never meant to be a discipline. It was a workaround - a temporary bridge between human intent and machine comprehension.
From Instruction to Intent
Early language models required precise instruction. You had to think like the machine to get useful output. Chain-of-thought prompting, few-shot examples, system prompts with elaborate personas - all compensations for model limitations.
With each generation, models get better at understanding what you mean, not just what you say. The gap between instruction and intent narrows.
What Replaces It
System Architecture. The ability to design workflows where AI and human judgment interact productively. Not the art of the perfect prompt, but the science of the perfect system.
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