Vol. 3 No. 2 (2023): July−December 2023
Short Articles

Rise of LLM-Driven Systems: Architecting Adaptive Software with Generative AI

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  • Aditya S Shethiya
Aditya S Shethiya
University of Bridgeport, Connecticut, USA

Published 2023-08-16

Keywords

  • Generative AI,
  • Large Language Models,
  • Software Architecture,
  • Adaptive Systems,
  • Prompt Engineering

How to Cite

Shethiya, A. S. (2023). Rise of LLM-Driven Systems: Architecting Adaptive Software with Generative AI. Spectrum of Research, 3(2). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/23

Copyright (c) 2023 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The integration of Generative AI, particularly Large Language Models (LLMs), into software systems is redefining how applications are designed, built, and maintained. Unlike traditional deterministic components, LLMs introduce adaptability, reasoning, and natural language understanding, enabling intelligent user interactions, dynamic content generation, and autonomous workflows. This paper explores the architectural principles and design patterns necessary to harness the potential of LLMs in modern software systems. It delves into the engineering challenges of incorporating non-deterministic behavior, prompt orchestration, and scalable deployment. We present a comprehensive framework for building adaptive software using LLMs while addressing the trade-offs in performance, observability, and ethical alignment. The goal is to provide software architects and developers with insights and strategies for shaping the next generation of intelligent, LLM-driven systems.

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