Context-Instrumental Data Distillation for Kubernetes Manifest Generation: Method and Experimental Evaluation
arXiv:2605.25835v1 Announce Type: new Abstract: This paper examines the specialization of Small Language Models (SLMs) with up to 4 billion parameters for generating artifacts in domain-specific languages (DSL). Kubernetes manifests are chosen as the target domain. We propose the context-instrumental data distillation method: the source corpus is formed through synthetic generation and, in an extended scheme, through reverse instruction generation from real Kubernetes YAML files, with pairs included in training only upon passing external validators and matching the domain context model. Unlike
The paper addresses the growing need for efficient and specialized AI tools to manage complex cloud-native infrastructure, reflecting current industry trends toward automation and optimization.
This research advances the practical application of small language models for generating domain-specific configurations, which can significantly enhance developer productivity and system reliability in cloud environments.
The proposed method offers a more robust and context-aware approach to generating critical infrastructure definitions, moving beyond general-purpose models for highly specialized tasks.
- · Cloud infrastructure providers
- · DevOps engineers
- · Kubernetes users
- · Companies operating large microservice architectures
- · Manual configuration specialists
- · Inefficient CI/CD pipelines
- · General-purpose code generation tools
Automated generation of Kubernetes manifests reduces human error and speeds up deployment cycles.
Increased reliance on specialized AI agents could lead to new vulnerabilities if models are not rigorously validated and secured.
The success of this approach could accelerate the development of similar context-instrumental distillation methods for other domain-specific languages and enterprise IT tasks, further enabling 'AI Agents' within organizations.
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Read at arXiv cs.LG