用反馈循环提升LLM生成基础设施代码的准确性
Using a Feedback Loop for LLM-based Infrastructure as Code Generation
- 引入错误反馈循环，让LLM根据提示持续优化代码
- 每轮迭代效果递减，最终趋于稳定不再提升
- 适合需要自动化部署且容忍少量缺陷的开发团队
大型语言模型（LLMs）在代码生成方面提升了开发者效率，但在代码周边任务如基础设施管理上影响有限。本文研究了基于LLM的智能体使用基础设施即代码（IaC）范式构建基础设施的能力。特别关注将生成代码中的错误与警告反馈回模型以改进输出的闭环机制。实验发现，每轮反馈迭代的效果呈指数下降，最终达到平台期，此后改进失效。
原文摘要 · Abstract (English)
Code generation with Large Language Models (LLMs) has helped to increase software developer productivity in coding tasks, but has yet to have significant impact on the tasks of software developers that surround this code. In particular, the challenge of infrastructure management remains an open question. We investigate the ability of an LLM agent to construct infrastructure using the Infrastructure as Code (IaC) paradigm. We particularly investigate the use of a feedback loop that returns errors and warnings on the generated IaC to allow the LLM agent to improve the code. We find that, for each iteration of the loop, its effectiveness decreases exponentially until it plateaus at a certain point and becomes ineffective.
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