{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:42:53Z","timestamp":1760060573899,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Background: Container orchestration systems like Kubernetes rely heavily on declarative manifest files, which serve as orchestration blueprints. However, managing these manifest files is often complex and requires substantial DevOps expertise. Methodology: This study investigates the use of Large Language Models (LLMs) to automate the creation of Kubernetes manifest files from natural language specifications, utilizing prompt engineering techniques within an innovative error- and warning-report\u2013aware refinement process. We assess the capabilities of these LLMs using Zero-Shot, Few-Shot, Prompt-Chaining, and Self-Refine methods to address DevOps needs and support fully automated deployment pipelines. Results: Our findings show that LLMs can generate Kubernetes manifests with varying levels of manual intervention. Notably, GPT-4 and GPT-3.5 demonstrate strong potential for deployment automation. Interestingly, smaller models sometimes outperform larger ones, challenging the assumption that larger models always yield better results. Conclusions: This research highlights the crucial impact of prompt engineering on LLM performance for Kubernetes tasks and recommends further exploration of prompt techniques and model comparisons, outlining a promising path for integrating LLMs into automated deployment workflows.<\/jats:p>","DOI":"10.3390\/fi17090416","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T10:50:04Z","timestamp":1757587804000},"page":"416","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prompt-Driven and Kubernetes Error Report-Aware Container Orchestration"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1660-8437","authenticated-orcid":false,"given":"Niklas","family":"Beuter","sequence":"first","affiliation":[{"name":"Expert Group AI in Applications, Institute for Interactive Systems, L\u00fcbeck University of Applied Sciences, 23562 L\u00fcbeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9496-2531","authenticated-orcid":false,"given":"Andr\u00e9","family":"Drews","sequence":"additional","affiliation":[{"name":"Expert Group AI in Applications, Institute for Interactive Systems, L\u00fcbeck University of Applied Sciences, 23562 L\u00fcbeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5130-4969","authenticated-orcid":false,"given":"Nane","family":"Kratzke","sequence":"additional","affiliation":[{"name":"Expert Group AI in Applications, Institute for Interactive Systems, L\u00fcbeck University of Applied Sciences, 23562 L\u00fcbeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kratzke, N. 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