IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection
Bridging the Correctness-Congruence Gap in LLM-Generated Infrastructure as Code
Large Language Models (LLMs) often struggle to generate correct and intent-aligned Infrastructure as Code (IaC). This research systematically injected structured configuration knowledge into LLM-based Terraform generation. We developed a novel error taxonomy and enhanced a benchmark with cloud emulation. Our methods, including advanced Graph RAG, significantly boosted technical validation success from 27.1% to 75.3% and overall success to 62.6%. However, we identified a 'Correctness-Congruence Gap,' where LLMs can act as proficient 'coders' but remain limited 'architects' for nuanced user intent, highlighting the need for deeper architectural reasoning.
Key Research Outcomes
Our study reveals significant advancements and critical insights into improving LLM performance for Infrastructure as Code.
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Baseline LLM Overall Success
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Overall Success with GR-LLMSum
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Technical Validation Success (Graph RAG)
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Technical Validation Increase (Baseline to Graph RAG)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Correctness-Congruence Gap
LLMs as Coders vs. Architects for IaC
Insufficient Parametric Knowledge: LLMs struggle to recall rigid, symbolic rules of provider schemas. Knowledge graphs improve reliability by anchoring probabilistic outputs in a deterministic source of truth, significantly reducing hallucination. This indicates that reliability in formal domains like IaC depends not on scaling models further, but on building architectures that incorporate external, grounded reasoning.
Principle of Optimal Context: Simply adding more context is not always beneficial; noisy or excessive context can harm performance. Effective systems should optimize context rather than maximize it, as demonstrated by GR-Ref's 'cognitive overload' effect on simpler tasks.
Enterprise Process Flow
Enhanced IaC-Eval Benchmark
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Error Taxonomy Development
→
Knowledge Injection Techniques (Naive RAG & Graph RAG)
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Evaluation & Analysis of Results
Impact of Knowledge Injection on IaC Generation
| Metric | No RAG (Base) | Naive RAG | Graph RAG (GR-LLMSum) |
|---|---|---|---|
| TV Pass Rate (%) | 37.2% | 70.2% | 83.2% |
| IV Pass Rate (on TV passes) (%) | 72.9% | 74.8% | 75.3% |
| Overall Success Rate (%) | 27.1% | 52.5% | 62.6% |
| Notes: TV: Technical Validation. IV: Intent Validation. Graph RAG (GR-LLMSum) represents the best performing Graph RAG enhancement. | |||
Misaligned Resource Selection: The Serverless MSK Cluster Example
Scenario: When prompted to create a 'serverless MSK cluster with 3 broker nodes in us-east-1' (Prompt #210, p. 16), the LLM generated an aws_msk_cluster resource instead of the correct aws_msk_serverless_cluster.
Challenge: This illustrates the LLM's difficulty with fine-grained semantic disambiguation between similar resource types, even when explicitly prompted. Such errors often stem from overgeneralization, where the model defaults to a more common resource type.
Solution Insight: Structured knowledge (Graph RAG) with semantic enrichment helps in selecting the correct resource, bridging the gap between technical validity and user intent. This highlights the need for LLMs to move beyond simple translation to architectural reasoning.
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