Research Papers
Explore the latest academic research and publications on PromptFolding™ protocol and related technologies
Featured Papers
PromptFolding™: A Novel Hierarchical Architecture for Large Language Model Prompt Engineering
Dr. Lisa Chan, Dr. Marcus Rodriguez, Prof. Emily Watson
Stanford AI Lab • December 2024
Nature Machine Intelligence
This paper introduces PromptFolding™, a revolutionary hierarchical architecture that enables efficient, scalable, and maintainable prompt engineering for large language models. We demonstrate significant improvements in prompt performance, interpretability, and reusability across multiple domains.
Performance Analysis of Hierarchical Prompt Architectures in Production Environments
Dr. Emily Watson, Dr. Marcus Rodriguez
UC Berkeley • October 2024
IEEE Transactions on Artificial Intelligence
This study analyzes the performance characteristics of hierarchical prompt architectures in real-world production environments. We provide empirical evidence of improved latency, throughput, and resource utilization compared to traditional approaches.
Security and Privacy Considerations in Hierarchical Prompt Systems
Dr. Michael Chen, Dr. Lisa Chan
Princeton University • July 2024
USENIX Security Symposium
This paper examines security and privacy implications of hierarchical prompt architectures. We identify potential vulnerabilities and propose mitigation strategies for secure deployment in sensitive environments.
All Research Papers
PromptFolding™: A Novel Hierarchical Architecture for Large Language Model Prompt Engineering
Dr. Lisa Chan, Dr. Marcus Rodriguez, Prof. Emily Watson
Stanford AI Lab • December 2024
Nature Machine Intelligence
This paper introduces PromptFolding™, a revolutionary hierarchical architecture that enables efficient, scalable, and maintainable prompt engineering for large language models. We demonstrate significant improvements in prompt performance, interpretability, and reusability across multiple domains.
Scalable Prompt Management: A Systematic Approach to Enterprise AI Deployment
Dr. Alex Thompson, Dr. Lisa Chan
MIT CSAIL • November 2024
ACM Transactions on Software Engineering
We present a comprehensive framework for managing prompts at enterprise scale, addressing challenges in version control, testing, deployment, and monitoring. Our approach reduces prompt-related incidents by 73% and improves development velocity by 2.4x.
Performance Analysis of Hierarchical Prompt Architectures in Production Environments
Dr. Emily Watson, Dr. Marcus Rodriguez
UC Berkeley • October 2024
IEEE Transactions on Artificial Intelligence
This study analyzes the performance characteristics of hierarchical prompt architectures in real-world production environments. We provide empirical evidence of improved latency, throughput, and resource utilization compared to traditional approaches.
PromptFolding™ Protocol: Formal Specification and Implementation Guidelines
Dr. Lisa Chan, Dr. David Kim
Carnegie Mellon University • September 2024
Computer Science Research Repository (CoRR)
We present the formal specification of the PromptFolding™ protocol, including mathematical foundations, implementation guidelines, and security considerations. This work establishes the theoretical framework for the protocol's widespread adoption.
Cross-Domain Prompt Reusability: A Study Using PromptFolding™ Architecture
Prof. Lisa Zhang, Dr. Alex Thompson
University of Toronto • August 2024
Proceedings of ACL 2024
We investigate the reusability of prompts across different domains using the PromptFolding™ architecture. Our results show that hierarchical prompt structures enable 67% better cross-domain transfer compared to flat prompt designs.
Security and Privacy Considerations in Hierarchical Prompt Systems
Dr. Michael Chen, Dr. Lisa Chan
Princeton University • July 2024
USENIX Security Symposium
This paper examines security and privacy implications of hierarchical prompt architectures. We identify potential vulnerabilities and propose mitigation strategies for secure deployment in sensitive environments.
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