Ambig-IaC: Multi-level Disambiguation for Interactive Cloud Infrastructure-as-Code Synthesis
Source: arXiv:2604.02382v1
Announce Type: cross
Introduction
The growing scale and complexity of modern cloud infrastructure has revolutionized the way organizations manage their deployments. Infrastructure-as-Code (IaC) has emerged as a critical methodology enabling developers and DevOps teams to automate and streamline infrastructure management. As large language models (LLMs) become increasingly sophisticated, they are now being utilized to generate IaC configurations from natural language inputs. However, one significant challenge remains: user requests are often underspecified, leading to ambiguity in the generated configurations.
The Challenge of Ambiguity in IaC
Unlike traditional code generation, IaC configurations cannot be executed cheaply or iteratively repaired, forcing LLMs into an almost one-shot regime. This lack of flexibility exacerbates the issues arising from ambiguity, making it imperative to develop effective methods for disambiguating user requests. Our research identifies that ambiguity in IaC presents a tractable compositional structure. Specifically, configurations can be decomposed into three hierarchical axes:
- Resources: The fundamental building blocks of the infrastructure, such as virtual machines, databases, and networking components.
- Topology: The arrangement and relationships between different resources, defining how they interact and communicate.
- Attributes: The specific settings and configurations that define the behavior and properties of each resource.
This hierarchical structure indicates that higher-level decisions significantly constrain lower-level ones, creating a framework for targeted disambiguation.
Introducing Ambig-IaC
To address these challenges, we propose a novel framework called Ambig-IaC. This training-free, disagreement-driven approach generates diverse candidate specifications based on user prompts. Key features of Ambig-IaC include:
- Diverse Candidate Generation: The framework produces multiple potential IaC configurations to reflect the ambiguity of user requests.
- Structural Disagreement Identification: It identifies disagreements across the hierarchical axes of resources, topology, and attributes.
- Informativeness Ranking: Candidate configurations are ranked based on their informativeness, allowing for more effective narrowing of the configuration space.
- Targeted Clarification Questions: The framework generates questions that help clarify user intent, progressively refining the specifications.
Benchmark and Evaluation
We introduce a benchmark consisting of 300 validated IaC tasks with ambiguous prompts, designed to evaluate the performance of different IaC generation methods. Our evaluation framework employs graph edit distance and embedding similarity to assess structural and attribute accuracy. The results are promising: our method outperforms the strongest baseline, achieving relative improvements of +18.4% and +25.4% on structure and attribute evaluations, respectively.
Conclusion
As organizations increasingly adopt cloud technologies, the need for effective IaC generation methods will only continue to grow. The Ambig-IaC framework represents a significant step forward in addressing the challenges of ambiguity in IaC, paving the way for more efficient and accurate infrastructure management. By combining innovative techniques in AI and natural language processing, we aim to enhance the user experience and streamline cloud infrastructure provisioning.