The core principle is prompt engineering via template-based abstraction, where LLM interactions are formalized through predefined structures that separate static context from dynamic variables using closure functions to manage state and scope. This mechanism utilizes functional programming concepts like closures to encapsulate API client states within a chat interface factory function, enabling the modular construction of prompts for specific tasks such as email generation without modifying underlying logic during runtime execution. The theory posits that separating prompt formulation via templating engines from direct text input allows for systematic control over variable injection, tone specification, and role assignment while maintaining code reusability across different Large Language Model instances.
The core principle is prompt engineering via template-based abstraction, where LLM interactions are formalized through predefined structures that separate static context from dynamic variables using …