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About this paper
Jules White
Jules White proposes a conceptual framework for 'living software systems' that dynamically adapt to user needs and contexts, leveraging generative and agentic AI as universal translators. This approach aims to reduce the translation burden between human intent and computer operations, enabling more adaptive, accessible, and rapidly evolving software.
Key insights
- Generative AI, particularly large language models (LLMs), can serve as universal translators, converting human intent into machine actions and system outputs into human understanding across various technical domains.
- The vision of 'living software systems' can be pursued through two pathways: accelerating traditional software development with AI tools or developing directly with adaptive, agentic AI systems.
- Prompt engineering becomes a critical skill for users and developers to effectively define objectives, provide context, specify constraints, and iteratively refine AI-generated outputs for dynamic software.
Summary by alphaXiv.
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