提出分层架构，让大模型系统开发更高效可靠。
A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems
- 将大模型系统开发分为不同层级，各层专注特定能力。
- 通过案例验证，该架构提升系统可扩展性和工程效率。
- 适合需要构建稳定大模型应用的开发者参考。
大规模语言模型（LLMs）已超越基础语言任务，广泛应用于各类软件系统。然而，随着应用需求不断演进，其原生能力常难以满足实际要求。为应对挑战，需采用多种方法增强模型表现，如调节推理温度或设计激发创造力的提示词。不同方法在工程复杂度、可扩展性及运行成本上存在权衡。本文提出一种分层架构，将大模型系统开发划分为多个具有特定属性的层级，使能力实现更具系统性与效率，从而支持所需功能与质量目标。通过多个实际案例研究，验证了该框架的有效性。本工作为开发者在构建基于大模型的软件系统时，提供了选择合适技术的实用指导，有助于提升系统的鲁棒性与可扩展性。
原文摘要 · Abstract (English)
Significant efforts has been made to expand the use of Large Language Models (LLMs) beyond basic language tasks. While the generalizability and versatility of LLMs have enabled widespread adoption, evolving demands in application development often exceed their native capabilities. Meeting these demands may involve a diverse set of methods, such as enhancing creativity through either inference temperature adjustments or creativity-provoking prompts. Selecting the right approach is critical, as different methods lead to trade-offs in engineering complexity, scalability, and operational costs. This paper introduces a layered architecture that organizes LLM software system development into distinct layers, each characterized by specific attributes. By aligning capabilities with these layers, the framework encourages the systematic implementation of capabilities in effective and efficient ways that ultimately supports desired functionalities and qualities. Through practical case studies, we illustrate the utility of the framework. This work offers developers actionable insights for selecting suitable technologies in LLM-based software system development, promoting robustness and scalability.
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