Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work

TL;DR

This paper introduces Knowledge Protocol Engineering (KPE), a new paradigm that systematically converts expert knowledge into machine-executable protocols, enabling LLMs to perform complex, domain-specific reasoning and tasks.

Contribution

It presents KPE as a novel methodology for translating natural language expert knowledge into operational protocols, enhancing LLMs' domain-specific reasoning capabilities.

Findings

KPE enables LLMs to perform complex, multi-step domain tasks.

It bridges the gap between factual retrieval and logical reasoning.

KPE has potential applications in law, bioinformatics, and beyond.

Abstract

The capabilities of Large Language Models (LLMs) have opened new frontiers for interacting with complex, domain-specific knowledge. However, prevailing methods like Retrieval-Augmented Generation (RAG) and general-purpose Agentic AI, while powerful, often struggle with tasks that demand deep, procedural, and methodological reasoning inherent to expert domains. RAG provides factual context but fails to convey logical frameworks; autonomous agents can be inefficient and unpredictable without domain-specific heuristics. To bridge this gap, we introduce Knowledge Protocol Engineering (KPE), a new paradigm focused on systematically translating human expert knowledge, often expressed in natural language documents, into a machine-executable Knowledge Protocol (KP). KPE shifts the focus from merely augmenting LLMs with fragmented information to endowing them with a domain's intrinsic logic, operational strategies, and methodological principles. We argue that a well-engineered Knowledge Protocol allows a generalist LLM to function as a specialist, capable of decomposing abstract queries and executing complex, multi-step tasks. This position paper defines the core principles of KPE, differentiates it from related concepts, and illustrates its potential applicability across diverse fields such as law and bioinformatics, positing it as a foundational methodology for the future of human-AI collaboration.