Academic Article: Instruction architecture in Generative AI: A framework for prompt engineering and reasoning control in Large Language Models (LLM)
Abstract
The rapid use of large language models (LLMs), such as ChatGPT, Gemini, Claude, Grok, DeepSeek, Perplexity, Elicit, and others, has transformed human-computer interaction by prioritizing natural language processing over conventional programming methods. The introduced paradigm change emphasizes the essential talent of rapid production, which can determine the correctness, reliability, inventiveness, and safety of AI outputs. It is now a matter of introducing prompt engineering as a methodological field and its significance in practical implementation in various disciplines of research, education, and decision-making. Despite its extensive utilization, the evolution of instant invention has been inadequately organized, sometimes regarded as an ad hoc or intuitive process. The article provides a comprehensive, methodical, and pragmatic examination of prompt creation, integrating theoretical foundations of contemporary AI architectures with practical prompting strategies. It entails the assessment of architectural, cognitive, and probabilistic concepts underlying contemporary AI systems. The taxonomy of prompts, design methodology, optimization, and evaluation methodologies is applied to real-world scenarios in research, software engineering, education, and decision-making. The monograph aims to establish immediate invention as a methodological discipline, contrasting it with an undisciplined creative form.
Keywords
Prompt engineering, Large language models, AI Reasoning, Generative AI, Instruction Design
LINK: actainnovations.com
