We introduce Knowledge Protocol Engineering (KPE), a novel approach designed to enhance AI's capability beyond mere factual recall to practical, domain-specific execution. It argues that while current AI, like Large Language Models (LLMs), excels at memorizing information (often augmented by Retrieval-Augmented Generation, or RAG) and can utilize tools (agent systems), it frequently lacks the procedural know-how of human experts, leading to unreliable outcomes in complex tasks. KPE proposes a systematic method to translate human expertise—found in manuals and standard operating procedures—into machine-executable protocols, essentially teaching AI "how to think" within a specific field. This involves extracting expert knowledge, encoding it into structured templates, guiding the AI's execution of tasks based on these rules, and validating the output against predefined steps. The benefits of KPE include increased procedural precision, rapid specialization of AI without extensive retraining, enhanced transparency and auditability, and a shift in human roles towards "protocol engineering", where domain experts directly program AI behavior.

Knowledge Protocol Engineering: Teaching AI the How, Not Just the What

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