Prompt Engineering: Theory and Practice

Abstract

Prompt Engineering is an emerging discipline that has gained visibility with the arrival of generative artificial intelligence (AI). It is the most natural form of interaction with Large Language Models (LLMs) such as ChatGPT, Gemini, and Claude, among others. This work presents a structured analysis of best practices for crafting prompts, based on a systematic review of recent scientific literature. The proposed framework, called "Anatomy of a Prompt," identifies and details eight fundamental components for creating effective prompts: role/persona, context, specificity, structure, examples, decomposition, output format, and iteration. Evidence suggests that the systematic application of these components results in significant improvements in the quality of responses generated by LLMs, with gains of up to 57% in accuracy. Additionally, an analysis of the intrinsic relationships between these components is presented, organized into three functional layers: knowledge domain definition, narrative enhancement, and result specification. This work demonstrates a need for a structured mental model that surpasses the traditional keyword search paradigm, positioning Prompt Engineering as an essential competency in the era of generative AI.

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