Ambig-IaC: Multi-level Disambiguation for Interactive Cloud Infrastructure-as-Code Synthesis

Abstract

The scale and complexity of modern cloud infrastructure have made Infrastructure-as-Code (IaC) essential for managing deployments. While large Language models (LLMs) are increasingly being used to generate IaC configurations from natural language, user requests are often underspecified. Unlike traditional code generation, IaC configurations cannot be executed cheaply or iteratively repaired, forcing the LLMs into an almost one-shot regime. We observe that ambiguity in IaC exhibits a tractable compositional structure: configurations decompose into three hierarchical axes (resources, topology, attributes) where higher-level decisions constrain lower-level ones. We propose a training-free, disagreement-driven framework that generates diverse candidate specifications, identifies structural disagreements across these axes, ranks them by informativeness, and produces targeted clarification questions that progressively narrow the configuration space. We introduce \textsc{Ambig-IaC}, a benchmark of 300 validated IaC tasks with ambiguous prompts, and an evaluation framework based on graph edit distance and embedding similarity. Our method outperforms the strongest baseline, achieving relative improvements of +18.4\% and +25.4\% on structure and attribute evaluations, respectively.

Figures (4)

• Figure 1: An underspecified user request corresponds to many plausible cloud infrastructures that can be represented as resource dependency graphs (left). Ambiguity arises in choosing resource abstractions and services for compute, networking, databases, etc., inter-resource topology, and per-resource configuration attributes when translating intent into Infrastructure-as-Code (right).
• Figure 2: Overview of the iterative multi-level disambiguation process for interactive Infrastructure-as-Code synthesis. Users provide an initial IaC request. Initially, a pool of diverse structured specifications will be generated based on the current interpretations. Symbolically compute disagreements among the current pool of candidates. These disagreements are ranked to identify the most informative differences measured by entropy. Based on the top-ranked disagreement, the system generates a targeted clarification question. Interaction with the user. If the candidate pool is non-empty, prune candidates inconsistent with the user's answer. The refined candidate pool is fed back into the next iteration, repeating the process until the interaction budget is exhausted. Otherwise, pool regeneration will be triggered.
• Figure 3: Per-round dynamics.
• Figure 4: Regeneration analysis.