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Working paper introduces a framework for improving AI collaboration through structured external cognitive architectures, suggesting future development.
The Distributed Cognitive Prompting Framework for AI Collaboration (DCPF) is a structured framework that organizes AI-assisted work through persistent external cognitive architectures rather than relying solely on increasingly complex prompts. The framework distributes cognitive functions across interconnected Research, Knowledge, Runtime, and Governance Frameworks that collectively improve continuity, consistency, scalability, and maintainability in AI collaboration. This working paper presents the theoretical foundation, architectural design, and initial implementation of DCPF while outlining its intended application to complex, multi-stage AI projects. The framework is designed to support iterative refinement through validation testing, practical implementation, and future research. DCPF emphasizes reusable knowledge structures, modular framework design, sequential execution protocols, and persistent information management to enable more reliable and transparent human-AI collaboration across a broad range of domains. This publication represents Version 1.0 of the Distributed Cognitive Prompting Framework for AI Collaboration and serves as the foundational reference for ongoing validation studies, future framework development, and subsequent publications.
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Perez, Ralph A., Jr. (2026) studied this question.
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