Prompt Theory: A Framework

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Prompt Theory: A Unified Framework for Understanding AI Prompting and Human Cognition

Preprint

GitHub

Abstract

We introduce Prompt Theory, a foundational framework that establishes structural isomorphisms between artificial intelligence prompting systems and human neurobiological input-output mechanisms. By formalizing the parallels between prompt design and cognitive input processing, we reveal shared emergent properties, failure modes, and optimization pathways across both domains. Our contribution is threefold: (1) a formal mapping between AI prompting and human cognitive processing stages, from input encoding to output generation; (2) identification of recursive patterns that govern both systems, including attention allocation, context management, and drift phenomena; and (3) a unified mathematical framework for optimizing prompts based on neurobiologically-inspired principles. Experimental results demonstrate that prompt designs informed by this framework yield significant improvements in AI system performance across multiple benchmarks. We posit that prompt engineering should be reconceptualized as a fundamental locus of intelligence amplification at the human-AI boundary, with implications for cognitive science, AI alignment, and human-computer interaction.

Keywords: Prompt Theory, Large Language Models, Cognitive Science, Human-AI Interaction, Attention Mechanisms, Recursive Systems, Emergence

[smithblack-0]

Well that complements SUPS and the broader Constitutional Philosophical Self-Play process I am working on nicely.

What I am trying to do is use self-play with careful sheparding to bootstrap a simple ethics and reasoning system into a complex one by reflection, working on a hypothesized ‘goldilocks’ zone where the problems are simple enough for the model to solve, but hard enough to learn from.

And all that is holy, it exists. It is when 0.65< Te/Tc < 0.85. Fantastic - models can in fact bootstrap their cognition and alignment by self-play and generation of synthetic training data if you can keep it in that range.

I just have to finish SUPS and then hook up the main algorithm, and I can likely build more robust formal feedback mechanisms now. Thank you very much for sharing, this greatly boosts my confidence it will work.

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Prompt Theory Going Viral

Justine Moore

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