Everything Is Prompt Engineering

Everything Is Prompt Engineering: A Formal Argument Author's note : This post proposes a falsifiable thesis and attempts a rigorous proof. If you've shipped production AI systems, this won't teach you to write better prompts. It tries to answer a more fundamental question: what exactly are you doing when you build all of this? The final section responds directly to the strongest objections, including emergence theory, multimodal heterogeneity, and the dynamic-weight challenge. 0. The Thesis Proposition P : Within the current Transformer-based large language model paradigm, all Workflow, Agent, MCP, Skill, Harness, and Context mechanisms are computationally equivalent to prompt engineering of varying complexity. This sounds trivial. It isn't. "Equivalent" here doesn't mean "similar" or "analogous"—it means there exists a behavior-preserving reduction: any such mechanism can be fully expressed as a strategy for constructing the model's input token sequence, without loss of behavioral cap