Distilling LLM Behavior: How Meta-Prompting Extracts Transferable Design Principles

Prompt engineering is typically practiced as a craft: iterative, tacit, and difficult to transfer. This article describes a methodology — meta-prompting for behavior specification — in which iterative prompt refinement is coupled, at each step, with a generalization phase: when a specific fix is found that corrects a behavioral failure, the model is immediately asked to extract a general rule from that fix, scoped to the task at hand. Repeated across many refinement cycles, this loop produces an accumulating set of grounded, transferable design principles — not just a better prompt for one task, but a structured guide for producing good prompts across a class of tasks. The methodology is described, the structure and quality of the extracted principles are examined using a case study in slot-filling conversational AI design, and implications are discussed for how practitioners might treat prompt engineering as a more systematic and documentable discipline.