Large Language Models (LLMs) currently exhibit low success rates in generating correct and intent-aligned Infrastructure as Code (IaC). This research investigated methods to improve LLM-based IaC generation, specifically for Terraform, by systematically injecting structured configuration knowledge. To facilitate this, an existing IaC-Eval benchmark was significantly enhanced with cloud emulation and automated error analysis. Additionally, a novel error taxonomy for LLM-assisted IaC code generation was developed. A series of knowledge injection techniques was implemented and evaluated, progressing from Naive Retrieval-Augmented Generation (RAG) to more sophisticated Graph RAG approaches. These included semantic enrichment of graph components and modeling inter-resource dependencies. Experimental results demonstrated that while baseline LLM performance was poor (27.1% overall success), injecting structured configuration knowledge increased technical validation success to 75.3% and overall success to 62.6%. Despite these gains in technical correctness, intent alignment plateaued, revealing a "Correctness-Congruence Gap" where LLMs can become proficient "coders" but remain limited "architects" in fulfilling nuanced user intent.

翻译：当前，大语言模型在生成正确且意图对齐的基础设施即代码方面成功率较低。本研究通过系统性地注入结构化配置知识，探索了改进基于大语言模型的基础设施即代码生成的方法，特别针对Terraform平台。为此，我们在现有IaC-Eval基准测试中大幅增强了云仿真与自动化错误分析功能。此外，本研究还提出了一种针对大语言模型辅助基础设施即代码生成的新型错误分类体系。我们实施并评估了一系列知识注入技术，从基础的检索增强生成方法逐步演进至更复杂的图检索增强生成方案，包括图组件的语义增强与跨资源依赖关系建模。实验结果表明：虽然基线大语言模型性能较差（总体成功率27.1%），但注入结构化配置知识可将技术验证成功率提升至75.3%，总体成功率提升至62.6%。尽管技术正确性显著提升，意图对齐度却出现平台期，这揭示了“正确性-一致性鸿沟”——大语言模型能成为熟练的“编码者”，但在满足复杂用户意图方面仍受限于“架构师”角色。