Background: Container orchestration systems such as Kubernetes rely heavily on declarative manifest files that serve as blueprints for orchestration. However, managing these manifest files often presents significant challenges and requires considerable expertise in DevOps. Methodology. This study explores the use of Large Language Models (LLMs) to automate the generation of Kubernetes manifest files using natural language specifications and prompt engineering techniques. We evaluate the effectiveness of these LLMs through Zero-Shot, Few-Shot, Prompt-Chaining, and Self-Refine methods to fulfill DevOps requirements and facilitate fully automated deployment pipelines. Results. The results indicate that LLMs can produce Kubernetes manifests with varying degrees of manual input, with GPT-4 and GPT-3.5 demonstrating potential for fully automated deployments. Interestingly, smaller models sometimes outperform larger ones, challenging the assumption that larger models are always superior. Conclusion: The research highlights the critical role of prompt engineering in enhancing LLM outputs for Kubernetes and suggests further research into prompt strategies and LLM performance comparisons, presenting a promising direction for integrating LLMs into automated deployment workflows.