You asked a large-language model to generate a Kubernetes Deployment manifest for an intranet web service. The first response works, but it uses the deprecated extensions/v1beta1 API version and does not set any CPU or memory limits. According to prompt-engineering best practices referenced in the Linux+ objective on AI, which follow-up prompt is most likely to guide the model toward a corrected, production-ready manifest with the least trial-and-error?
"Reset this conversation and give me a fresh Kubernetes Deployment manifest instead."
"Fix anything wrong in the manifest you just wrote."
"Please update your previous manifest by replacing extensions/v1beta1 with apps/v1 and adding reasonable CPU and memory limits to every container, then return the complete YAML."
"Regenerate the manifest and make it fully production-ready."
Effective prompt engineering is iterative: you supply the model with concrete feedback about what was wrong and instruct it to revise the earlier result. The option that names the two specific defects (deprecated API version and missing resource limits) and directs the model to return an updated manifest gives the model the contextual information it needs and avoids vague language. Simply regenerating without details, starting a new chat, or telling the model to "fix anything wrong" lacks the clarity and specificity needed for reliable improvement.
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What is the difference between `extensions/v1beta1` and `apps/v1` in Kubernetes?
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